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• An Introduction to Evolutionary Computation in Practice.- Design for Product Embedded Disassembly.- Multi-Level Decomposition for Tractability in Structural Design Optimization.- Representing the Change-Free Form Deformation for Evolutionary Design Optimization.- Evolving Microstructured Optical Fibres.- Making Interactive Evolutionary Graphic Design Practical.- Optimization of Store Performance using Personalized Pricing.- A Computational Intelligence Approach to Railway Track Intervention Planning.- A Co-Evolutionary Fuzzy System for Reservoir Well Logs Interpretation.- Resource Scheduling with Permutation Based Representations.- Evolutionary Computation in the Chemical Industry.- Technology Transfer: Academia to Industry.- A Survey of Practitioners of Evolutionary Computation.- Evolutionary Computation Applications: Twelve Lessons Learned.- Evolutionary Computation at American Air Liquide.
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This book is loaded with examples in which computer scientists and engineers have used evolutionary computation-programs that mimic natural evolution-to solve real problems. They aren't abstract, mathematically intensive papers, but accounts of solving important problems, including tips from the authors on how to avoid common pitfalls, maximize the effectiveness and efficiency of the search process, and many other practical suggestions. Some of the authors have already won "Humies"--Human Competitive Results Awards-for the work described in this book. I highly recommend it as a highly concentrated source of good problem-solving approaches that are applicable to many real-world problems. --Erik Goodman, Vice President, Red Cedar Technology, Inc.; Professor, Electrical & Computer Engineering, Michigan State University; and Founding Chair, ACM SIGEVO, the Special Interest Group on Genetic and Evolutionary Computation of the Association for Computing Machinery

• Part I Theory.- Adaptation of a Success Story in GAs: Estimation-of-Distribution Algorithms for Tree-based Optimization Problems.- The Automated Design of Artificial Neural Networks Using Evolutionary Computation.- A Versatile Surrogate-Assisted Memetic Algorithm for Optimization of Computationally.- Expensive Functions and its Engineering Applications.- Data Mining and Intelligent Multi Agent Technologies in Medical Informatics.- Part II Applications.- Evolving Trading Rules.- A hybrid genetic algorithm for the protein folding problem using the 2D-HP lattice model.- Optimal Management of Agricultural Systems.- Evolutionary Electronics: Automatic Synthesis of Analog Circuits by Gas.- Intuitive Visualization and Interactive Analysis of Pareto Sets Applied on Production Engineering Systems.- Privacy Protection with Genetic Algorithms.- A revision of Evolutionary Computation techniques in telecommunications and an application for the network global planning problem.- Survivable Network Design with an Evolution Strategy.- Evolutionary Computations for Design Optimization and Test Automation in VLSI Circuits.- Evolving Cooperative Agents in Economy Market using Genetic Algorithms.- Optimizing Multiplicative General Parameter Finite Impulse Response Filters Using Evolutionary Computation.- Applying Genetic Algorithms to optimize the cost of multiple sourcing supply chain systems-An industry case study.
• (source: Nielsen Book Data)

Darwinian evolutionary theory is one of the most important theories in human history for it has equipped us with a valuable tool to understand the amazing world around us. There can be little surprise, therefore, that Evolutionary Computation (EC), inspired by natural evolution, has been so successful in providing high quality solutions in a large number of domains. EC includes a number of techniques, such as Genetic Algorithms, Genetic Programming, Evolution Strategy and Evolutionary Programming, which have been used in a diverse range of highly successful applications. This book brings together some of these EC applications in fields including electronics, telecommunications, health, bioinformatics, supply chain and other engineering domains, to give the audience, including both EC researchers and practitioners, a glimpse of this exciting rapidly evolving field.
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Contents
• Applications of Evolutionary Computation
• An Enhanced Opposition-Based Evolutionary Feature Selection Approach
• 1 Introduction
• 2 Moth Flame Optimization
• 2.1 Binary Moth Flame Optimization
• 2.2 Binary Moth Flame Optimization for Feature Selection
• 3 The Proposed Approach
• 3.1 Initialization Using Opposition-Based Method
• 3.2 Retiring Flame
• 4 Experimental Setup and Results
• 5 Conclusions
• References
• A Methodology for Determining Ion Channels from Membrane Potential Neuronal Recordings
• 1 Introduction

• 2 Conductance-Based Model Description
• 3 Defining a Benchmark with Known Types of Ion Channels
• 4 Methodology and Experimental Setup
• 5 Experimental Results
• 6 Conclusions
• A Mathematical Description of the Models
• B Experimental Setup and Parameter Ranges
• References
• Swarm Optimised Few-View Binary Tomography
• 1 Introduction
• 2 Binary Tomographic Reconstruction
• 3 Swarm Optimisation
• 4 Constrained Search in High Dimensions
• 5 Reconstructions
• 6 Results
• 7 Discussion
• 8 Conclusions
• References

• Comparing Basin Hopping with Differential Evolution and Particle Swarm Optimization
• 1 Introduction
• 2 The Metaheuristics Studied
• 2.1 Basin Hopping
• 2.2 Differential Evolution
• 2.3 Particle Swarm Optimization
• 3 The Benchmarking Environment
• 4 Experimental Setup
• 5 Experimental Results
• 6 Conclusions
• References
• Combining the Properties of Random Forest with Grammatical Evolution to Construct Ensemble Models
• 1 Introduction
• 2 Methodology
• 2.1 Structured Grammatical Evolution
• 2.2 Random Structured Grammatical Evolution for Symbolic Regression Problems

• 3 Experimental Setup
• 3.1 Study Problems
• 3.2 Configuration of the Algorithms
• 4 Results
• 5 Conclusions
• References
• EvoCC: An Open-Source Classification-Based Nature-Inspired Optimization Clustering Framework in Python
• 1 Introduction
• 2 Related Works
• 3 Methodology
• 4 Framework Overview
• 4.1 Parameters
• 4.2 Datasets
• 4.3 Clustering with EvoCluster
• 4.4 Classification
• 4.5 Evaluation Measures
• 4.6 Results Management
• 5 Experiments and Visualizations
• 6 Conclusion and Future Works
• References
• Evolution of Acoustic Logic Gates in Granular Metamaterials

• 1 Introduction
• 2 Problem Statement
• 3 Simulation Setup
• 3.1 2D Granular Simulator
• 3.2 Optimization Method
• 4 Results and Discussion
• 4.1 Evolution of an Acoustic Band Gap
• 4.2 Evolving an AND Gate
• 4.3 Evolving an XOR Gate
• 5 Conclusion and Future Work
• References
• Public-Private Partnership: Evolutionary Algorithms as a Solution to Information Asymmetry
• 1 Introduction
• 2 The Problem
• 3 Proposed Approach
• 3.1 The Model
• 3.2 Data
• 3.3 Adversarial Optimization
• 3.4 Operator (EA1)
• 3.5 Public Administration (EA2)
• 4 Experimental Evaluation

This book constitutes the refereed proceedings of the 25th International Conference on Applications of Evolutionary Computation, EvoApplications 2022, held as part of Evo*2022, in April 2022, co-located with the Evo*2022 events EuroGP, EvoCOP, and EvoMUSART. The 46 revised full papers presented in this book were carefully reviewed and selected from 67 submissions. .
(source: Nielsen Book Data)

• On Restricting Real-Valued Genotypes in Evolutionary Algorithms.- Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions.- Co-Optimising Robot Morphology and Controller in a Simulated Open-ended Environment.- Multi-objective workforce allocation in construction projects.- Generating Duplex Routes for Robust Bus Transport Network by Improved Multi-objective Evolutionary Algorithm based on Decomposition.- Combining Multi-objective Evolutionary Algorithms with deep generative models towards focused molecular design.- A Multi-Objective Evolutionary Algorithm Approach for Optimizing Part Quality Aware Assembly Job Shop Scheduling Problems.- Evolutionary Grain-Mixing to Improve Profitability in Farming Winter Wheat.- Automatic Modular Design of Behavior Trees for Robot Swarms with Communication Capabilites.- Salp Swarm Optimization Search Based Feature Selection for Enhanced Phishing Websites Detection.- Real Time Optimisation of Traffic Signals to Prioritise Public Transport.- Adaptive Covariance Pattern Search.- Evaluating the Success-History based Adaptive Differential Evolution in the Protein Structure Prediction problem.- Beyond Body Shape and Brain: Evolving the Sensory Apparatus of Voxel-based Soft Robots.- Desirable Objective Ranges in Preference-based Evolutionary Multiobjective Optimization.- Improving Search Efficiency and Diversity of Solutions in Multiobjective Binary Optimization by Using Metaheuristics plus Integer Linear Programming.- Automated, Explainable Rule Extraction from MAP-Elites archives.- EDM-DRL: Toward Stable Reinforcement Learning through Ensembled Directed Mutation.- Continuous Ant-Based Neural Topology.- Playing with Dynamic Systems - Battling Swarms in Virtual Reality.- EvoCraft: A New Challenge for Open-Endedness.- A Profile-Based 'GrEvolutionary' Hearthstone Agent.- Modelling Asthma Patients' Responsiveness to Treatment Using Feature Selection and Evolutionary Computation.- Bayesian Networks for Mood Prediction Using Unobtrusive Ecological Momentary Assessments.- A Multi-Objective Multi-Type Facility Location Problem for the Delivery of Personalised Medicine.- RDE-OP: A Region-Based Differential Evolution Algorithm Incorporation Opposition-Based Learning for Optimising the Learning Process of Multi-Layer Neural Networks.- Estimation of Grain-level Residual Stresses in a Quenched Cylindrical Sample of Aluminum Alloy AA5083 using Genetic Programming.- EDA-based optimization of blow-off valve positions for centrifugal compressor systems.- 3D-2D Registration using X-ray Simulation and CMA-ES.- Lateralized Approach for Robustness AgainstAttacks in Emotion Categorization from Images.- Improved Crowding Distance in Multi-objective Optimization for Feature Selection in Classification.- Deep Optimisation: Multi-Scale Evolution by Inducing and Searching in Deep Representations.- Evolutionary Planning in Latent Space.- Utilizing the Untapped Potential of Indirect Encoding for Neural Networks with Meta Learning.- Effective Universal Unrestricted Adversarial Attacks using a MOE Approach.- Improving Distributed Neuroevolution Using Island Extinction and Repopulation.- An Experimental Study of Weight Initialization and Lamarckian Inheritance on Neuroevolution.- Towards Feature-Based Performance Regression Using Trajectory Data.- Demonstrating the Evolution of GANs through t-SNE.- Optimising diversity in classifier ensembles of classification trees.- WILDA: Wide Learning of Diverse Architectures for Classification of Large Datasets.- Evolving Character-Level DenseNet Architectures using Genetic Programming.- Transfer Learning for Automated Test Case Prioritization using XCSF.- On the Effects of Absumption for XCS with Continuous-Valued Inputs.- A NEAT Visualisation of Neuroevolution Trajectories.- Evaluating Models with Dynamic Sampling Holdout.- Event-driven multi-algorithm optimization: mixing Swarm and Evolutionary strategies.- TensorGP - Genetic Programming Engine in TensorFlow.- A novel evolutionary approach for IoT-based water contaminant detection.- Evolutionary Algorithms for Roughness Coefficient Estimation in River Flow Analyses.- EA-based ASV Trajectory Planner for Pollution Detection in Lentic Waters.
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This book constitutes the refereed proceedings of the 24th International Conference on Applications of Evolutionary Computation, EvoApplications 2021, held as part of Evo*2021, as Virtual Event, in April 2021, co-located with the Evo*2021 events EuroGP, EvoCOP, and EvoMUSART. The 51 revised full papers presented in this book were carefully reviewed and selected from 78 submissions. The papers cover a wide spectrum of topics, ranging from applications of evolutionary computation; applications of deep bioinspired algorithms; soft computing applied to games; machine learning and AI in digital healthcare and personalized medicine; evolutionary computation in image analysis, signal processing and pattern recognition; evolutionary machine learning; parallel and distributed systems; and applications of nature inspired computing for sustainability and development. .
(source: Nielsen Book Data)

• Applications of Evolutionary Computation.- A Local Search for Numerical Optimisation based on Covariance Matrix Diagonalisation.- EvoCluster: An Open-Source Nature-Inspired Optimization Clustering Framework in Python.- Optimizing the Hyperparameters of a Mixed Integer Linear Programming Solver to Speed Up Electric Vehicle Charging Control.- Automatic rule extraction from access rules using Genetic Programming.- Search Trajectory Networks of Population-based Algorithms in Continuous Spaces.- Evolving-controllers versus learning-controllers for morphologically evolvable robots.- Simulation-driven multi-objective evolution for traffic light optimization.- Automatic Generation of Adversarial Metamorphic Malware Using MAP-Elites.- EvoDynamic: a framework for the evolution of generally represented dynamical systems and its application to criticality.- A Decomposition-Based Evolutionary Algorithm with Adaptive Weight Vectors for Multi- and Many-objective Optimization.- Differential Evolution Multi-Objective for Tertiary Protein Structure Prediction.- Particle Swarm Optimization: A Wrapper-based Feature Selection Method for Ransomware Detection and Classification.- A method for estimating the computational complexity of multimodal functions.- Locating Odour Sources with Geometric Syntactic Genetic Programming.- Designing cable-stayed bridges with Genetic Algorithms.- A fast, scalable meta-heuristic for network slicing under traffic uncertainty.- What is Your MOVE: Modeling Adversarial Network Environments.- Using evolution to design modular robots: An empirical approach to select module designs.- Iterated Granular Neighborhood Algorithm for the Taxi Sharing Problem.- Applications of Bio-inspired techniques on Social Networks.- Multiobjective Optimization of a Targeted Vaccination Scheme in the Presence of Non-diagnosed Cases.- Community Detection in Attributed Graphs with Differential Evolution.- Applications of Deep Bioinspired Algorithms.- Fake news detection using time series and user features classification.- Social Learning vs Self-teaching in a Multi-agent Neural Network System.- Evolving Instinctive Behaviour in Resource-Constrained Autonomous Agents Using Grammatical Evolution.- An Adversarial Optimization Approach for the Development of Robust Controllers.- Soft Computing Applied to Games.- Efficient Heuristic Policy Optimisation for a Challenging Strategic Card Game.- Finding Behavioural Patterns Among League of Legends Players Through Hidden Markov Models.- Learning the Designer's Preferences to Drive Evolution.- Testing hybrid computational intelligence algorithms for general game playing.- Evolutionary Computation in Digital Healthcare and Personalized Medicine.- Accelerated Design of HIFU Treatment Plans Using Island-based Evolutionary Strategy.- Using Genetic Algorithms for the prediction of cognitive impairments.- Short and Medium Term Blood Glucose Prediction using Multi-Objective Grammatical Evolution.- Evolutionary Machine Learning.- A Greedy Iterative Layered Framework for Training Feed Forward Neural Networks.- Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution.- An Empirical Exploration of Deep Recurrent Connections Using Neuro-Evolution.- Using Skill Rating as Fitness on the Evolution of GANs.- A Local Search with a Surrogate Assisted Option for Instance Reduction.- Evolutionary Latent Space Exploration of Generative Adversarial Networks.- Neuro-Evolutionary Transfer Learning through Structural Adaptation.- Ant-based Neural Topology Search (ANTS) for Optimizing Recurrent Networks.- Parallel and Distributed Systems.- A MIMD interpreter for Genetic Programming.- Security Risk Optimization for Multi-Cloud Applications.- Using evolutionary algorithms for server hardening via the moving target defense technique.- An Event-based Architecture for Cross-Breed Multi-population Bio-inspired Optimization Algorithms.
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This book constitutes the refereed proceedings of the 23rd European Conference on Applications of Evolutionary Computation, EvoApplications 2020, held as part of Evo*2020, in Seville, Spain, in April 2020, co-located with the Evo*2020 events EuroGP, EvoMUSART and EvoCOP. The 44 full papers presented in this book were carefully reviewed and selected from 62 submissions. The papers cover a wide spectrum of topics, ranging from applications of bio-inspired techniques on social networks, evolutionary computation in digital healthcare and personalized medicine, soft-computing applied to games, applications of deep-bioinspired algorithms, parallel and distributed systems, and evolutionary machine learning. .
(source: Nielsen Book Data)

• 1 Evolutionary Computation in Economics and Finance: An Overview of the Book.- 2 Playing Games with Genetic Algorithms.- 3 Genetic Algorithm Learning and Economic Evolution.- 4 Using Symbolic Regression to Infer Strategies from Experimental Data.- 5 The Efficiency of an Artificial Double Auction Stock Market with Neural Learning Agents.- 6 On AIE-ASM: Software to Simulate Artificial Stock Markets with Genetic Programming.- 7 Exchange Rate Volatility in the Artificial Foreign Exchange Market.- 8 Using an Artificial Market Approach to Analyze Exchange Rate Scenarios.- 9 Emulating Trade in Emissions Permits: An Application of Genetic Algorithms.- 10 Cooperative Computation with Market Mechanism.- 11 Hysteresis in an Evolutionary Labor Market with Adaptive Search.- 12 Computable Learning, Neural Networks and Institutions.- 13 On Two Types of GA-Learning.- 14 Evolutionary Computation and Economic Models: Sensitivity and Unintended Consequences.- 15 Tinkering with Genetic Algorithms: Forecasting and Data Mining in Finance and Economics.- 16 Forecasting Ability But No Profitability: An Empirical Evaluation of Genetic Algorithm-Optimised Technical Trading Rules.- 17 Evolutionary Induction of Trading Models.- 18 Optimizing Technical Trading Strategies with Split Search Genetic Algorithms.- 19 GP Forecasts of Stock Prices for Profitable Trading.- 20 Option Pricing Via Genetic Programming.- 21 Evolutionary Computation in Option Pricing: Determining Implied Volatilities Based on American Put Options.- 22 Evolutionary Computation in Economics and Finance: A Bibliography.
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After a decade's development, evolutionary computation (EC) proves to be a powerful tool kit for economic analysis. While the demand for this equipment is increasing, there is no volume exclusively written for economists. This volume for the first time helps economists to get a quick grasp on how EC may support their research. A comprehensive coverage of the subject is given, that includes the following three areas: game theory, agent-based economic modelling and financial engineering. Twenty leading scholars from each of these areas contribute a chapter to the volume. The reader will find himself treading the path of the history of this research area, from the fledgling stage to the burgeoning era. The results on games, labour markets, pollution control, institution and productivity, financial markets, trading systems design and derivative pricing, are new and interesting for different target groups. The book also includes informations on web sites, conferences, and computer software.
(source: Nielsen Book Data)

• Evolutionary and Memetic Computing for Project Portfolio Selection and Scheduling: An Introduction.- Evolutionary Approaches for Project Portfolio Optimization: An Overview.- An Introduction to Evolutionary and Memetic Algorithms for Parameter Optimization.- An Overall Characterization of the Project Portfolio Optimization Problem and an Approach Based on Evolutionary Algorithms to Address It.- A New Model for the Project Portfolio Selection and Scheduling Problem with Defence Capability Options.- Analysis of New Approaches used in Portfolio Optimization: A Systematic Literature Review.- A Temporal Knapsack Approach to Defence Portfolio Selection.- A Decision Support System for Planning Portfolios of Supply Chain Improvement Projects in the Semiconductor Industry.
• (source: Nielsen Book Data)

This book consists of eight chapters, authored by distinguished researchers and practitioners, that highlight the state of the art and recent trends in addressing the project portfolio selection and scheduling problem (PPSSP) across a variety of domains, particularly defense, social programs, supply chains, and finance. Many organizations face the challenge of selecting and scheduling a subset of available projects subject to various resource and operational constraints. In the simplest scenario, the primary objective for an organization is to maximize the value added through funding and implementing a portfolio of projects, subject to the available budget. However, there are other major difficulties that are often associated with this problem such as qualitative project benefits, multiple conflicting objectives, complex project interdependencies, workforce and manufacturing constraints, and deep uncertainty regarding project costs, benefits, and completion times. It is well known that the PPSSP is an NP-hard problem and, thus, there is no known polynomial-time algorithm for this problem. Despite the complexity associated with solving the PPSSP, many traditional approaches to this problem make use of exact solvers. While exact solvers provide definitive optimal solutions, they quickly become prohibitively expensive in terms of computation time when the problem size is increased. In contrast, evolutionary and memetic computing afford the capability for autonomous heuristic approaches and expert knowledge to be combined and thereby provide an efficient means for high-quality approximation solutions to be attained. As such, these approaches can provide near real-time decision support information for portfolio design that can be used to augment and improve existing human-centric strategic decision-making processes. This edited book provides the reader with a broad overview of the PPSSP, its associated challenges, and approaches to addressing the problem using evolutionary and memetic computing.
(source: Nielsen Book Data)

• Optimal Resource Sharing amongst Device-to-Device Communication using Particle Swarm Algorithm.- Real Time Human Locator and Advance Home Security Appliances.- A Novel Implementation of Haptic Robotic Arm.- Design of Book Recommendation System Using Sentiment Analysis.- Review of Python for Solar Photovoltaic Systems.- Key Stroke Dynamics for User Verification.- The Role of Predictive Data Analytics in Retailing.- Improved Scheduling Algorithm for Load Balancing in Cloud Computing.- Generating Automobile Images Dynamically from Text Description.- Generating Automobile Images Dynamically from Text Description.- Identification of Online Auction Bidding Robots using Machine Learning.- Analysis of Attention Deficit Hyperactivity Disorder using Various Classifiers.- Generating Feasible Path Between Path Testing and Data Flow Testing.- Parking Management System Using Internet of Things.- Binomial Logistic Regression Resource Optimized Routing In MANET.- Homo Sapiens Sapiens Diabetes Mellitus Detection and Classification.- Improved Logistic Map based Algorithm for Biometric Image Encryption.- Accuracy Analysis of Similarity Measures in Surprise Framework.- Tunable Access Control for Data Sharing in Cloud.- Conceptual Online Education using E-Learning Platform of Cloud Computing.- Efficient Deployment of Web Application in Serverless Environment.
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This book features selected research papers presented at the International Conference on Evolutionary Computing and Mobile Sustainable Networks (ICECMSN 2020), held at the Sir M. Visvesvaraya Institute of Technology on 20-21 February 2020. Discussing advances in evolutionary computing technologies, including swarm intelligence algorithms and other evolutionary algorithm paradigms which are emerging as widely accepted descriptors for mobile sustainable networks virtualization, optimization and automation, this book is a valuable resource for researchers in the field of evolutionary computing and mobile sustainable networks.
(source: Nielsen Book Data)

• Evolutionary Multi-Multi-Objective Optimization - EMMOO.- Implementation of Multiobjective Memetic Algorithms for Combinatorial Optimization Problems: A Knapsack Problem Case Study.- Knowledge Infused in Design of Problem-Specific Operators.- Solving Time-Tabling Problems Using Evolutionary Algorithms and Heuristics Search.- An Efficient Genetic Algorithm with Uniform Crossover for the Multi-Objective Airport Gate Assignment Problem.- Application of Evolutionary Algorithms for Solving Multi-Objective Simulation Optimization Problems.- Feature Selection Using Single/Multi-Objective Memetic Frameworks.- Multi-Objective Robust Optimization Assisted by Response Surface Approximation and Visual Data-Mining.- Multiobjective Metamodel-Assisted Memetic Algorithms.- A Convergence Acceleration Technique for Multiobjective Optimisation.- Knowledge Propagation through Cultural Evolution.- Risk and Cost Tradeoff in Economic Dispatch Including Wind Power Penetration Based on Multi-Objective Memetic Particle Swarm Optimization.- Hybrid Behavioral-Based Multiobjective Space Trajectory Optimization.- Nature-Inspired Particle Mechanics Algorithm for Multi-Objective Optimization.- Information Exploited for Local Improvement.- Combination of Genetic Algorithms and Evolution Strategies with Self-adaptive Switching.- Comparison between MOEA/D and NSGA-II on the Multi-Objective Travelling Salesman Problem.- Integrating Cross-Dominance Adaptation in Multi-Objective Memetic Algorithms.- A Memetic Algorithm for Dynamic Multiobjective Optimization.- A Memetic Coevolutionary Multi-Objective Differential Evolution Algorithm.- Multiobjective Memetic Algorithm and Its Application in Robust Airfoil Shape Optimization.
• (source: Nielsen Book Data)

The application of sophisticated evolutionary computing approaches for solving complex problems with multiple conflicting objectives in science and engineering have increased steadily in the recent years. Within this growing trend, Memetic algorithms are, perhaps, one of the most successful stories, having demonstrated better efficacy in dealing with multi-objective problems as compared to its conventional counterparts. Nonetheless, researchers are only beginning to realize the vast potential of multi-objective Memetic algorithm and there remain many open topics in its design. This book presents a very first comprehensive collection of works, written by leading researchers in the field, and reflects the current state-of-the-art in the theory and practice of multi-objective Memetic algorithms. "Multi-Objective Memetic algorithms" is organized for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of Memetic algorithms and multi-objective optimization.
(source: Nielsen Book Data)

• Theory and Methodology.- An Evolutionary Algorithm for the Solution of Two-Variable Word Equations in Partially Commutative Groups.- Determining Whether a Problem Characteristic Affects Heuristic Performance.- Performance and Scalability of Genetic Algorithms on NK-Landscapes.- Engineering Stochastic Local Search Algorithms: A Case Study in Estimation-Based Local Search for the Probabilistic Travelling Salesman Problem.- Hybrid Approaches.- A Lagrangian Decomposition/Evolutionary Algorithm Hybrid for the Knapsack Constrained Maximum Spanning Tree Problem.- A Hybrid Optimization Framework for Cutting and Packing Problems.- A Hybrid Genetic Algorithm for the DNA Fragment Assembly Problem.- A Memetic-Neural Approach to Discover Resources in P2P Networks.- Constrained Problems.- An Iterative Heuristic Algorithm for Tree Decomposition.- Search Intensification in Metaheuristics for Solving the Automatic Frequency Problem in GSM.- Contraction-Based Heuristics to Improve the Efficiency of Algorithms Solving the Graph Colouring Problem.- Scheduling.- Different Codifications and Metaheuristic Algorithms for the Resource Renting Problem with Minimum and Maximum Time Lags.- A Simple Optimised Search Heuristic for the Job Shop Scheduling Problem.- Parallel Memetic Algorithms for Independent Job Scheduling in Computational Grids.- Routing and Travelling Salesman Problems.- Reducing the Size of Travelling Salesman Problem Instances by Fixing Edges.- Algorithms for Large Directed Capacitated Arc Routing Problem Instances.- An Evolutionary Algorithm with Distance Measure for the Split Delivery Capacitated Arc Routing Problem.- A Permutation Coding with Heuristics for the Uncapacitated Facility Location Problem.
• (source: Nielsen Book Data)

Combinatorial optimisation is a ubiquitous discipline whose usefulness spans vast applications domains. The intrinsic complexity of most combinatorial optimisation problems makes classical methods unaffordable in many cases. To acquire practical solutions to these problems requires the use of metaheuristic approaches that trade completeness for pragmatic effectiveness. Such approaches are able to provide optimal or quasi-optimal solutions to a plethora of difficult combinatorial optimisation problems. The application of metaheuristics to combinatorial optimisation is an active field in which new theoretical developments, new algorithmic models, and new application areas are continuously emerging. This volume presents recent advances in the area of metaheuristic combinatorial optimisation, with a special focus on evolutionary computation methods. Moreover, it addresses local search methods and hybrid approaches. In this sense, the book includes cutting-edge theoretical, methodological, algorithmic and applied developments in the field, from respected experts and with a sound perspective.
(source: Nielsen Book Data)

• Introduction to Optimization.- Classical Optimization Algorithms.- Evolutionary and Swarm Optimization.- Introduction to Machine Learning.- Data-Driven Surrogate-Assisted Evolutionary Optimization.- Multi-Surrogate-Assisted Single-Objective Optimization.- Surrogate-Assisted Multi-Objective Evolutionary Optimization.
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Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.
(source: Nielsen Book Data)

• Chapter 1. Introduction: Optimization and Metaheuristics Algorithms.-
• Chapter 2. Metaheuristics Paradigms for Renewable Energy Systems: Advances in Optimization Algorithms.-
• Chapter 3. Tackling Power Quality Issues using Metaheuristics.-
• Chapter 4. Meta-Heuristic application in Suppression of Noise.-
• Chapter 5. A review on Genetic Algorithm and its application in Power system Engineering.-
• Chapter 6. Different Variants of Particle Swarm Optimization Algorithms and its Application: A Review.-
• Chapter 7. Application of Metaheuristics in Power Electronics.-
• Chapter 8. Cuckoo Search Algorithm: A Review of Recent Variants and Engineering Applications.-
• Chapter 9. Energy Management System for Hybrid Energy System: Renewable Integration, modeling & optimization, control aspects and conceptual framework.-
• Chapter 10. Recent Advances and Application of Metaheuristic Algorithms: A survey (2014-2020).
• (source: Nielsen Book Data)

This book addresses the principles and applications of metaheuristic approaches in engineering and related fields. The first part covers metaheuristics tools and techniques such as ant colony optimization and Tabu search, and their applications to several classes of optimization problems. In turn, the book's second part focuses on a wide variety of metaheuristics applications in engineering and/or the applied sciences, e.g. in smart grids and renewable energy. In addition, the simulation codes for the problems discussed are included in an appendix for ready reference. Intended for researchers aspiring to learn and apply metaheuristic techniques, and gathering contributions by prominent experts in the field, the book offers readers an essential introduction to metaheuristics, its theoretical aspects and applications.
(source: Nielsen Book Data)

• On Monte Carlo Tree Search for Weighted Vertex Coloring.- A RNN-based Hyper-heuristic for combinatorial problems.- Algorithm Selection for the Team Orienteering Problem.- Performance evaluation of a parallel ant colony optimization for the real-time train routing selection problem in large instances.- Deep Infeasibility Exploration Method for Vehicle Routing Problems.- Evolutionary Algorithms for the Constrained Two-Level Role Mining Problem.- Simplifying Dispatching Rules in Genetic Programming for Dynamic Job Shop Scheduling.- Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems.- A Beam Search for the Shortest Common Supersequence Problem Guided by an Approximate Expected Length Calculation.- Modeling the Costas Array Problem in QUBO for Quantum Annealing.- Penalty Weights in QUBO formulations: Permutation Problems.- PUBOi: a tunable benchmark with variable importance.- Stagnation Detection meets Fast Mutation.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 22nd European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2022, held as part of Evo*2022, in Madrid, Spain, during April 20-21, 2022, co-located with the Evo*2022 events: EvoMUSART, EvoApplications, and EuroGP. The 13 revised full papers presented in this book were carefully reviewed and selected from 28 submissions. They present recent theoretical and experimental advances in combinatorial optimization, evolutionary algorithms, and related research fields.
(source: Nielsen Book Data)

• Genetic Algorithm for Optimization of Multiple Objectives in Knowledge Discovery from Large Databases.- Knowledge Incorporation in Multi-objective Evolutionary Algorithms.- Evolutionary Multi-objective Rule Selection for Classification Rule Mining.- Rule Extraction from Compact Pareto-optimal Neural Networks.- On the Usefulness of MOEAs for Getting Compact FRBSs Under Parameter Tuning and Rule Selection.- Classification and Survival Analysis Using Multi-objective.- Clustering Based on Genetic Algorithms.
• (source: Nielsen Book Data)

Data Mining (DM) is the most commonly used name to describe such computational analysis of data and the results obtained must conform to several objectives such as accuracy, comprehensibility, interest for the user etc. Though there are many sophisticated techniques developed by various interdisciplinary fields only a few of them are well equipped to handle these multi-criteria issues of DM. Therefore, the DM issues have attracted considerable attention of the well established multiobjective genetic algorithm community to optimize the objectives in the tasks of DM. The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD). These articles are written by leading experts around the world. It is shown how the different MOEAs can be utilized, both in individual and integrated manner, in various ways to efficiently mine data from large databases.
(source: Nielsen Book Data)

• to Evolutionary Computing in System Design.- Evolutionary Neuro-Fuzzy Systems and Applications.- Evolution of Fuzzy Controllers and Applications.- A Neuro-Genetic Framework for Multi-Classifier Design: An Application to Promoter Recognition in DNA Sequences.- Evolutionary Grooming of Traffic in WDM Optical Networks.- EPSO: Evolutionary Particle Swarms.- Design of Type-Reduction Strategies for Type-2 Fuzzy Logic Systems using Genetic Algorithms.- Designing a Recurrent Neural Network-based Controller for Gyro-Mirror Line-of-Sight Stabilization System using an Artificial Immune Algorithm.- Distributed Problem Solving using Evolutionary Learning in Multi-Agent Systems.- Evolutionary Computing within Grid Environment.- Application of Evolutionary Game Theory to Wireless Mesh Networks.- Applying Hybrid Multiobjective Evolutionary Algorithms to the Sailor Assignment Problem.- Evolutionary Techniques Applied to Hardware Optimization Problems: Test and Verification of Advanced Processors.
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Evolutionary computing paradigms offer robust and powerful adaptive search mechanisms for system design. This book's thirteen chapters cover a wide area of topics in evolutionary computing and applications, including an introduction to evolutionary computing in system design; evolutionary neuro-fuzzy systems; and evolution of fuzzy controllers. The book will be useful to researchers in intelligent systems with interest in evolutionary computing, as well as application engineers and system designers.
(source: Nielsen Book Data)

• Basic Concepts.- MOP Evolutionary Algorithm Approaches.- MOEA Local Search and Coevolution.- MOEA Test Suites.- MOEA Testing and Analysis.- MOEA Theory and Issues.- Applications.- MOEA Parallelization.- Multi-Criteria Decision Making.- Alternative Metaheuristics.
• (source: Nielsen Book Data)

This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems. It contains exhaustive appendices, index and bibliography and links to a complete set of teaching tutorials, exercises and solutions.
(source: Nielsen Book Data)

• An Introduction to Multi-Objective Evolutionary Algorithms and Their Applications
• Optimal Design of Industrial Electromagnetic Devices: A Multiobjective Evolutionary Approach
• Using a Particle Swarm Optimizer with a Multi-Objective Selection Scheme to Design Combinational Logic Circuits
• Automatic Control System Design via a Multiobjective Evolutionary Algorithm
• Evolutionary Multi-Objective Optimization of Trusses
• A Multi-Objective Evolutionary Algorithm for the Covering Tour Problem
• Multiobjective Aerodynamic Design and Visualization of Supersonic Wings by Using Adaptive Range Multiobjective Genetic Algorithms
• Mutli-Objective Spectroscopic Data Analysis of Inertial Confinement Fusion Implosion Cores: Plasma Gradient Determination
• On Machine Learning with Multiobjective Genetic Optimization
• and other papers.
• (source: Nielsen Book Data)

This book presents an extensive variety of multi-objective problems across diverse disciplines, along with statistical solutions using multi-objective evolutionary algorithms (MOEAs). The topics discussed serve to promote a wider understanding as well as the use of MOEAs, the aim being to find good solutions for high-dimensional real-world design applications. The book contains a large collection of MOEA applications from many researchers, and thus provides the practitioner with detailed algorithmic direction to achieve good results in their selected problem domain.
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• Front Matter; Engineering Evolutionary Intelligent Systems: Methodologies, Architectures and Reviews; Genetically Optimized Hybrid Fuzzy Neural Networks: Analysis and Design of Rule-based Multi-layer Perceptron Architectures; Genetically Optimized Self-organizing Neural Networks Based on Polynomial and Fuzzy Polynomial Neurons: Analysis and Design; Evolution of Inductive Self-organizing Networks; Recursive Pattern based Hybrid Supervised Training; Enhancing Recursive Supervised Learning Using Clustering and Combinatorial Optimization (RSL-CC).

Evolutionary design of intelligent systems is gaining much popularity due to its capabilities in handling several real world problems involving optimization, complexity, noisy and non-stationary environment, imprecision, uncertainty and vagueness. This edited volume "Engineering Evolutionary Intelligent Systems" deals with the theoretical and methodological aspects, as well as various evolutionary algorithm applications to many real world problems originating from science, technology, business or commerce. This volume comprises of 15 chapters including an introductory chapter which covers the fundamental definitions and outlines some important research challenges. The chapters were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed.
(source: Nielsen Book Data)

• Improved Grey wolf Optimization based Feature selection and classification using CNN for Diabetic Retinopathy detection.- Feature Selection Using Modified Sine Cosine Algorithm with COVID-19 Dataset.- Blood Cell Image Denoising based on Tunicate Rat Swarm Optimization with Median Filter.- A Hybrid Approach for Deep Noise Suppression using Deep Neural Networks.- Human Health Care Systems Analysis for Cloud Data Structure of Biometric System using ECG Analysis.- Data mining for Solving Medical Diagnostics Problems.- Classification of Diabetic Retinopathy using Ensemble of Machine Learning Classifiers with IDRID Dataset.- Epileptic Seizure Prediction Using Geometrical Features Extracted From HRV Signal.- An Extensive Survey on Outlier Prediction using Mining and Learning Approaches.- Performance Comparison of Data Security Strategies in Fog Computing.- Design and Simulation of a Direct-PSK Based Telecommand Receiver for Small Satellite
• Analysis of Data Aggregation and Clustering Protocol in Wireless Sensor Networks using Machine Learning.- DetecSec : A Framework to Detect and Mitigate ARP Cache Poisoning Attacks.- PAPR Reduction in SDR based OFDM System.
• (source: Nielsen Book Data)

This book mainly reflects the recent research works in evolutionary computation technologies and mobile sustainable networks with a specific focus on computational intelligence and communication technologies that widely ranges from theoretical foundations to practical applications in enhancing the sustainability of mobile networks. Today, network sustainability has become a significant research domain in both academia and industries present across the globe. Also, the network sustainability paradigm has generated a solution for existing optimization challenges in mobile communication networks. Recently, the research advances in evolutionary computing technologies including swarm intelligence algorithms and other evolutionary algorithm paradigms are considered as the widely accepted descriptors for mobile sustainable networks virtualization, optimization, and automation. To deal with the emerging impacts on mobile communication networks, this book discusses about the state-of-the research works on developing a sustainable design and their implementation in mobile networks. With the advent of evolutionary computation algorithms, this book contributes varied research chapters to develop a new perspective on mobile sustainable networks.
(source: Nielsen Book Data)

• Automated Flower Species Identification by using Deep convolution neural network
• Information Retrieval for Cloud Forensics
• Machine Translation System Combination with Enhanced Alignments using Word Embeddings
• Geometry Based Machining Feature Retrieval with Inductive Transfer Learning
• Grapheme to phoneme conversion for Malayalam Speech Using encoder-decoder Architecture
• Usage of Block Chain Technology in e-Voting System using Private Block Chain
• Bengali Visual Genome: A Multimodal Dataset for Machine Translation and Image Captioning
• Deep Learning based Mosquito Species detection using Wingbeats frequencies
• Developments in Capsule Network Architecture: A Review
• Computer aided segmentation of polyps using Mask R-CNN and approach to reduce false positives
• Image GPT with Super Resolution.

This book presents the proceedings of the 9th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2021), held at NIT Mizoram, Aizwal, Mizoram, India, during June 25-26, 2021. FICTA conference aims to bring together researchers, scientists, engineers, and practitioners to exchange their new ideas and experiences in the domain of intelligent computing theories with prospective applications to various engineering disciplines. This volume covers broad areas of Intelligent Data Engineering and Analytics. The conference papers included herein presents both theoretical as well as practical aspects of data intensive computing, data mining, big data, knowledge management, intelligent data acquisition and processing from sensors, data communication networks protocols and architectures, etc. The volume will also serve as a knowledge centre for students of post-graduate level in various engineering disciplines. .

• Theory.- It Is Hard to Distinguish Between Dominance Resistant Solutions and Extremely Convex Pareto Optimal Solutions.- On Analysis of Irregular Pareto Front Shapes.- On Statistical Analysis of MOEAs with Multiple Performance Indicators.- Algorithms.- Population Sizing of Evolutionary Large-Scale Multiobjective Optimization.- Kernel Density Estimation for Reliable Biobjective Solution of Stochastic Problems.- Approximating Pareto Fronts in Evolutionary Multiobjective Optimization with Large Population Size.- Multitask Feature Selection for Objective Reduction.- Embedding a Repair Operator in Evolutionary Single and Multi-Objective Algorithms - An Exploitation-Exploration Perspective.- Combining User Knowledge and Online Innovization for Faster Solution to Multi-Objective Design Optimization Problems.- Improving the Efficiency of R2HCA-EMOA.- Pareto Front Estimation Using Unit Hyperplane.- Towards Multi-Objective Co-Evolutionary Problem Solving.- MOEA/D for Multiple Multi-Objective Optimization.- Using a Genetic Algorithm-Based Hyper-heuristic to Tune MOEA/D for a Set of Benchmark Test Problems.- Diversity-Driven Selection Operator for Combinatorial Optimization.- Dynamic Multi-Objective Optimization.- An Online Machine Learning-Based Prediction Strategy for Dynamic Evolutionary Multi-Objective Optimization.- Generalized Test Suite for Continuous Dynamic Multi-Objective Optimization.- A Special Point and Transfer Component Analysis based Dynamic Multi-Objective Optimization Algorithm.- Constrained Multi-Objective Optimization.- An Improved Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization.- An Improved Epsilon Method with M2M for Solving Imbalanced CMOPs with Simultaneous Convergence-Hard and Diversity-Hard Constraints.- Constrained Bi-objective Surrogate-Assisted Optimization of Problems with Heterogeneous Evaluation Times: Expensive Objectives and Inexpensive Constraints.- SAMO-COBRA: A Fast Surrogate Assisted Constrained Multi-Objective Optimization Algorithm.- A Fast Converging Evolutionary Algorithm for Constrained Multiobjective Portfolio Optimization.- Manifold Learning Inspired Mating Restriction for Evolutionary Constrained Multiobjective Optimization.- Multi-Modal Optimization.- Mult
• i3: Optimizing Multimodal Single-Objective Continuous Problems in the Multi-Objective Space by Means of Multiobjectivization.- Niching Diversity Estimation for Multi-modal Multi-Objective Optimization.- Using Neighborhood-Based Density Measures for Multimodal Multi-Objective Optimization.- Many-Objective Optimization.- The (M-1)+1 Framework of Relaxed Pareto Dominance for Evolutionary Many-Objective Optimization.- Handling Priority Levels in Mixed Pareto-Lexicographic Many-Objective Optimization Problems.- Many-Objective Pathfinding based on Frechet Similarity Metric.- The Influence of Swarm Topologies in Many-Objective Optimization Problems.- Performance Evaluations and Empirical Studies.- An Overview of Pair-Potential Functions for Multi-Objective Optimization.- On the Parameter Setting of the Penalty-Based Boundary Intersection Method in MOEA/D.- A Comparison Study of Evolutionary Algorithms on Large-Scale Sparse Multi-Objective Optimization Problems.- EMO and Machine Learning.- Discounted Sampling Policy Gradient for Robot Multi-Objective Visual Control.- Lexicographic Constrained Multicriteria Ordered Clustering.- Local Search is a Remarkably Strong Baseline for Neural Architecture Search.- A Study on Realtime Task Selection based on Credit Information Updating in Evolutionary Multitasking.- Multi-Objective Neural Architecture Search with Almost No Training.- On the Interaction Between Distance Functions and Clustering Criteria in Multi-objective Clustering.- Surrogate Modeling and Expensive Optimization.- Investigating normalization bounds for hypervolume-based infill criterion for expensive multiobjective optimization.- Pareto-Based Bi-indicator Infill Sampling Criterion for Expensive Multiobjective Optimization.- MOEA/D with Gradient-Enhanced Kriging for Multiobjective Optimization.- Exploring Constraint Handling Techniques in Real-world Problems on MOEA/D with Limited Budget of Evaluations.- Dimension Dropout for Evolutionary High-Dimensional Expensive Multiobjective Optimization.- Multiobjective Optimization with Fuzzy Classification-assisted Environmental Selection.- Surrogate-Assisted Multi-Objective Particle Swarm Optimization for Building Energy Saving Design.- Solving Large-Scale Multi-Objective Optimization via Probabilistic Prediction Model.- MCDM and Interactive EMO.- An Artificial Decision Maker for Comparing Reference Point Based Interactive Evolutionary Multiobjective Optimization Methods.- To Boldly Show What No One Has Seen Before: A Dashboard for Visualizing Multi-objective Landscapes.- Interpretable Self-Organizing Maps (iSOM) for Visualization of Pareto Front in Multiple Objective Optimization.- Applications.- An Investigation of Decomposition-based Metaheuristics for Resource-Constrained Multi-objective Feature Selection in Software Product Lines.- Operator-Adapted Evolutionary Large-Scale Multiobjective Optimization for Voltage Transformer Ratio Error Estimation.- Multi-Objective Reinforcement Learning based Multi-Microgrid System Optimisation Problem.- Pareto Optimization for Influence Maximization in Social Networks.- Parallel Algorithms for Multiobjective Virtual Network Function Placement Problem.- Using Multi-Objective Grammar-based Genetic Programming to Integrate Multiple Social Theories in Agent-based Modeling.- Change Detection in SAR Images based on Evolutionary Multiobjective Optimization and Superpixel Segmentation.- Multi-Objective Emergency Resource Dispatch Based on Coevolutionary Multiswarm Particle Swarm Optimization.- Prediction of Blast Furnace Temperature Based on Evolutionary Optimization.- Multiobjective Optimization Design of Broadband Dual-Polarized Base Station Antenna.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 11th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2021 held in Shenzhen, China, in March 2021.The 47 full papers and 14 short papers were carefully reviewed and selected from 120 submissions. The papers are divided into the following topical sections: theory; algorithms; dynamic multi-objective optimization; constrained multi-objective optimization; multi-modal optimization; many-objective optimization; performance evaluations and empirical studies; EMO and machine learning; surrogate modeling and expensive optimization; MCDM and interactive EMO; and applications.
(source: Nielsen Book Data)

• Chapter 1. Design and Construction of a Dual Axis Solar Tracking System by Astronomical Algorithm.-
• Chapter 2. Estimation of Magnetic Flux linkage in SRM using various defuzzification techniques.-
• Chapter 3. Multilevel Inverter based STATCOM for Distribution System.-
• Chapter 4. Sensitivity Analysis and Design Optimization of Synchronous Reluctance and Permanent Magnet Motors.-
• Chapter 5. A New Heuristic algorithm for Economic Load Dispatch incorporating wind power.-
• Chapter 6. Enhanced Grasshopper Optimization Algorithm For Numerical Optimization.-
• Chapter 7. Eco-Routing - To Reduce Vehicle CO2 Emissions by CACC: An IoT Application.-
• Chapter 8. Fuzzy Sliding Mode Control of DC-DC Boost Converter with Right-Half Plane Zero.-
• Chapter 9. Liquid Level Control of Non Linear Process Using Big Bang - Big Crunch Optimization Based Controller.-
• Chapter 10. Impact of PV Cells and MPPT Controller on Power System Dynamics.-
• Chapter 11. Wavelet Feature Based Microcalcification Detection in Mammogram.-
• Chapter 12. Reliable Radiation Hardened Memory Cells for Single-Event Multiple Effects.-
• Chapter 13. Finger Vein Identification Using Deep Convolutional Generative Adversarial Networks.-
• Chapter 14. Computer Aided Detection of Malignant Mass in Mammogram using U-Net Architecture.-
• Chapter 15. Visualization and Evaluation of Methane Gas Leakage by Thermal Image Processing using Supervised Deep Learning Models.
• (source: Nielsen Book Data)

This book gathers selected papers presented at the 6th International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems, held at the Anna University, Chennai, India, from 20 to 22 April 2020. It covers advances and recent developments in various computational intelligence techniques, with an emphasis on the design of communication systems. In addition, it shares valuable insights into advanced computational methodologies such as neural networks, fuzzy systems, evolutionary algorithms, hybrid intelligent systems, uncertain reasoning techniques, and other machine learning methods and their application to decision-making and problem-solving in mobile and wireless communication networks.
(source: Nielsen Book Data)

• Introduction
• Preliminary
• Optinformatics Within a Single Problem Domain
• Optinformatics Across Heterogeneous Problem Domains and Solvers
• Potential Research Directions.

This book provides readers the recent algorithmic advances towards realizing the notion of optinformatics in evolutionary learning and optimization. The book also provides readers a variety of practical applications, including inter-domain learning in vehicle route planning, data-driven techniques for feature engineering in automated machine learning, as well as evolutionary transfer reinforcement learning. Through reading this book, the readers will understand the concept of optinformatics, recent research progresses in this direction, as well as particular algorithm designs and application of optinformatics. Evolutionary algorithms (EAs) are adaptive search approaches that take inspiration from the principles of natural selection and genetics. Due to their efficacy of global search and ease of usage, EAs have been widely deployed to address complex optimization problems occurring in a plethora of real-world domains, including image processing, automation of machine learning, neural architecture search, urban logistics planning, etc. Despite the success enjoyed by EAs, it is worth noting that most existing EA optimizers conduct the evolutionary search process from scratch, ignoring the data that may have been accumulated from different problems solved in the past. However, today, it is well established that real-world problems seldom exist in isolation, such that harnessing the available data from related problems could yield useful information for more efficient problem-solving. Therefore, in recent years, there is an increasing research trend in conducting knowledge learning and data processing along the course of an optimization process, with the goal of achieving accelerated search in conjunction with better solution quality. To this end, the term optinformatics has been coined in the literature as the incorporation of information processing and data mining (i.e., informatics) techniques into the optimization process. The primary market of this book is researchers from both academia and industry, who are working on computational intelligence methods and their applications. This book is also written to be used as a textbook for a postgraduate course in computational intelligence emphasizing methodologies at the intersection of optimization and machine learning. .
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• Preface to the Third Edition.Preface to the Second Edition.Preface to the First Edition.1 Defining Artificial Intelligence.1.1 Background.1.2 The Turing Test.1.3 Simulation of Human Expertise.1.3.1 Samuel's Checker Program.1.3.2 Chess Programs.1.3.3 Expert Systems.1.3.4 A Criticism of the Expert Systems or Knowledge-Based Approach.1.3.5 Fuzzy Systems.1.3.6 Perspective on Methods Employing Specific Heuristics.1.4 Neural Networks.1.5 Definition of Intelligence.1.6 Intelligence, the Scientific Method, and Evolution.1.7 Evolving Artificial Intelligence.References.
• Chapter 1 Exercises.2 Natural Evolution.2.1 The Neo-Darwinian Paradigm.2.2 The Genotype and the Phenotype: The Optimization of Behavior.2.3 Implications of Wright's Adaptive Topography: Optimization Is Extensive Yet Incomplete.2.4 The Evolution of Complexity: Minimizing Surprise.2.5 Sexual Reproduction.2.6 Sexual Selection.2.7 Assessing the Beneficiary of Evolutionary Optimization.2.8 Challenges to Neo-Darwinism.2.8.1 Neutral Mutations and the Neo-Darwinian Paradigm.2.8.2 Punctuated Equilibrium.2.9 Summary.References.
• Chapter 2 Exercises.3 Computer Simulation of Natural Evolution.3.1 Early Speculations and Specific Attempts.3.1.1 Evolutionary Operation.3.1.2 A Learning Machine.3.2 Artificial Life.3.3 Evolutionary Programming.3.4 Evolution Strategies.3.5 Genetic Algorithms.3.6 The Evolution of Evolutionary Computation.References.
• Chapter 3 Exercises.4 Theoretical and Empirical Properties of Evolutionary Computation.4.1 The Challenge.4.2 Theoretical Analysis of Evolutionary Computation.4.2.1 The Framework for Analysis.4.2.2 Convergence in the Limit.4.2.3 The Error of Minimizing Expected Losses in Schema Processing.4.2.3.1 The Two-Armed Bandit Problem.4.2.3.2 Extending the Analysis for "Optimally" Allocating Trials.4.2.3.3 Limitations of the Analysis.4.2.4 Misallocating Trials and the Schema Theorem in the Presence of Noise.4.2.5 Analyzing Selection.4.2.6 Convergence Rates for Evolutionary Algorithms.4.2.7 Does a Best Evolutionary Algorithm Exist?4.3 Empirical Analysis.4.3.1 Variations of Crossover.4.3.2 Dynamic Parameter Encoding.4.3.3 Comparing Crossover to Mutation.4.3.4 Crossover as a Macromutation.4.3.5 Self-Adaptation in Evolutionary Algorithms.4.3.6 Fitness Distributions of Search Operators.4.4 Discussion.References.
• Chapter 4 Exercises.5 Intelligent Behavior.5.1 Intelligence in Static and Dynamic Environments.5.2 General Problem Solving: Experiments with Tic-Tac-Toe.5.3 The Prisoner's Dilemma: Coevolutionary Adaptation.5.3.1 Background.5.3.2 Evolving Finite-State Representations.5.4 Learning How to Play Checkers without Relying on Expert Knowledge.5.5 Evolving a Self-Learning Chess Player.5.6 Discussion.References.
• Chapter 5 Exercises.6 Perspective.6.1 Evolution as a Unifying Principle of Intelligence.6.2 Prediction and the Languagelike Nature of Intelligence.6.3 The Misplaced Emphasis on Emulating Genetic Mechanisms.6.4 Bottom-Up Versus Top-Down.6.5 Toward a New Philosophy of Machine Intelligence.References.
• Chapter 6 Exercises.Glossary.Index.About the Author.
• (source: Nielsen Book Data)

This third edition provides the latest tools and techniques that enable computers to learn. The third edition of this internationally acclaimed publication provides the latest theory and techniques for using simulated evolution to achieve machine intelligence. As a leading advocate for evolutionary computation, the author has successfully challenged the traditional notion of artificial intelligence, which essentially programs human knowledge fact by fact, but does not have the capacity to learn or adapt as evolutionary computation does. Readers gain an understanding of the history of evolutionary computation, which provides a foundation for the author's thorough presentation of the latest theories shaping current research. Balancing theory with practice, the author provides readers with the skills they need to apply evolutionary algorithms that can solve many of today's intransigent problems by adapting to new challenges and learning from experience.Several examples are provided that demonstrate how these evolutionary algorithms learn to solve problems. In particular, the author provides a detailed example of how an algorithm is used to evolve strategies for playing chess and checkers. As readers progress through the publication, they gain an increasing appreciation and understanding of the relationship between learning and intelligence. Readers familiar with the previous editions will discover much new and revised material that brings the publication thoroughly up to date with the latest research, including the latest theories and empirical properties of evolutionary computation. The third edition also features new knowledge-building aids.Readers will find a host of new and revised examples. New questions at the end of each chapter enable readers to test their knowledge. Intriguing assignments that prepare readers to manage challenges in industry and research have been added to the end of each chapter as well. This is a must-have reference for professionals in computer and electrical engineering; it provides them with the very latest techniques and applications in machine intelligence. With its question sets and assignments, the publication is also recommended as a graduate-level textbook.
(source: Nielsen Book Data)

• Formal Theory
• New Techniques
• Experimental Analysis
• Multiobjective Optimization
• Hybrid Methods
• Applications.

This book constitutes the refereed proceedings of the 10th International Conference on Parallel Problem Solving from Nature, PPSN 2008, held in Dortmund, Germany, in September 2008. The 114 revised full papers presented were carefully reviewed and selected from 206 submissions. The conference covers a wide range of topics, such as evolutionary computation, quantum computation, molecular computation, neural computation, artificial life, swarm intelligence, artificial ant systems, artificial immune systems, self-organizing systems, emergent behaviors, and applications to real-world problems. The paper are organized in topical sections on formal theory, new techniques, experimental analysis, multiobjective optimization, hybrid methods, and applications.
(source: Nielsen Book Data)

• Parameter Setting in EAs: a 30 Year Perspective.- Parameter Control in Evolutionary Algorithms.- Self-Adaptation in Evolutionary Algorithms.- Adaptive Strategies for Operator Allocation.- Sequential Parameter Optimization Applied to Self-Adaptation for Binary-Coded Evolutionary Algorithms.- Combining Meta-EAs and Racing for Difficult EA Parameter Tuning Tasks.- Genetic Programming: Parametric Analysis of Structure Altering Mutation Techniques.- Parameter Sweeps for Exploring Parameter Spaces of Genetic and Evolutionary Algorithms.- Adaptive Population Sizing Schemes in Genetic Algorithms.- Population Sizing to Go: Online Adaptation Using Noise and Substructural Measurements.- Parameter-less Hierarchical Bayesian Optimization Algorithm.- Evolutionary Multi-Objective Optimization Without Additional Parameters.- Parameter Setting in Parallel Genetic Algorithms.- Parameter Control in Practice.- Parameter Adaptation for GP Forecasting Applications.
• (source: Nielsen Book Data)

One of the main difficulties of applying an evolutionary algorithm (or, as a matter of fact, any heuristic method) to a given problem is to decide on an appropriate set of parameter values. Typically these are specified before the algorithm is run and include population size, selection rate, operator probabilities, not to mention the representation and the operators themselves. This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. The book covers a broad area of evolutionary computation, including genetic algorithms, evolution strategies, genetic programming, estimation of distribution algorithms, and also discusses the issues of specific parameters used in parallel implementations, multi-objective evolutionary algorithms, and practical consideration for real-world applications. It is a recommended read for researchers and practitioners of evolutionary computation and heuristic methods.
(source: Nielsen Book Data)

• List of Contributors vii Editors' Biographies xi Preface xv Acknowledgments xvii
• 1 Evolutionary Computation in Scheduling: A Scientometric Analysis 1 Amir H. Gandomi, Ali Emrouznejad, and Iman Rahimi
• 2 Role and Impacts of Ant Colony Optimization in Job Shop Scheduling Problems: A Detailed Analysis 11 P. Deepalakshmi and K. Shankar
• 3 Advanced Ant Colony Optimization in Healthcare Scheduling 37 Reza Behmanesh, Iman Rahimi, Mostafa Zandieh, and Amir H. Gandomi
• 4 Task Scheduling in Heterogeneous Computing Systems Using Swarm Intelligence 73 S. Sarathambekai and K. Umamaheswari
• 5 Computationally Efficient Scheduling Schemes for Multiple Antenna Systems Using Evolutionary Algorithms and Swarm Optimization 105 Prabina Pattanayak and Preetam Kumar
• 6 An Efficient Modified Red Deer Algorithm to Solve a Truck Scheduling Problem Considering Time Windows and Deadline for Trucks' Departure 137 Amir Mohammad Fathollahi-Fard, Abbas Ahmadi, and Mohsen S. Sajadieh
• 7 Application of Sub-Population Scheduling Algorithm in Multi-Population Evolutionary Dynamic Optimization 169 Javidan Kazemi Kordestani and Mohammad Reza Meybodi
• 8 Task Scheduling in Cloud Environments: A Survey of Population-Based Evolutionary Algorithms 213 Fahimeh Ramezani, Mohsen Naderpour, Javid Taheri, Jack Romanous, and Albert Y. Zomaya
• 9 Scheduling of Robotic Disassembly in Remanufacturing Using Bees Algorithms 257 Jiayi Liu, Wenjun Xu, Zude Zhou, and Duc Truong Pham
• 10 A Modified Fireworks Algorithm to Solve the Heat and Power Generation Scheduling Problem in Power System Studies 299 Mohammad Sadegh Javadi, Ali Esmaeel Nezhad, Seyed-Ehsan Razavi, Abdollah Ahmadi, and Joao P.S. Catalao Index 327.
• (source: Nielsen Book Data)

Presents current developments in the field of evolutionary scheduling and demonstrates the applicability of evolutionary computational techniques to solving scheduling problems This book provides insight into the use of evolutionary computations (EC) in real-world scheduling, showing readers how to choose a specific evolutionary computation and how to validate the results using metrics and statistics. It offers a spectrum of real-world optimization problems, including applications of EC in industry and service organizations such as healthcare scheduling, aircraft industry, school timetabling, manufacturing systems, and transportation scheduling in the supply chain. It also features problems with different degrees of complexity, practical requirements, user constraints, and MOEC solution approaches. Evolutionary Computation in Scheduling starts with a chapter on scientometric analysis to analyze scientific literature in evolutionary computation in scheduling. It then examines the role and impacts of ant colony optimization (ACO) in job shop scheduling problems, before presenting the application of the ACO algorithm in healthcare scheduling. Other chapters explore task scheduling in heterogeneous computing systems and truck scheduling using swarm intelligence, application of sub-population scheduling algorithm in multi-population evolutionary dynamic optimization, task scheduling in cloud environments, scheduling of robotic disassembly in remanufacturing using the bees algorithm, and more. This book: Provides a representative sampling of real-world problems currently being tackled by practitioners Examines a variety of single-, multi-, and many-objective problems that have been solved using evolutionary computations, including evolutionary algorithms and swarm intelligence Consists of four main parts: Introduction to Scheduling Problems, Computational Issues in Scheduling Problems, Evolutionary Computation, and Evolutionary Computations for Scheduling Problems Evolutionary Computation in Scheduling is ideal for engineers in industries, research scholars, advanced undergraduates and graduate students, and faculty teaching and conducting research in Operations Research and Industrial Engineering.
(source: Nielsen Book Data)

• Evolutionary Algorithms for Data Mining and Knowledge Discovery.- Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining.- GAP: Constructing and Selecting Features with Evolutionary Computing.- Multi-Agent Data Mining using Evolutionary Computing.- A Rule Extraction System with Class-Dependent Features.- Knowledge Discovery in Data Mining via an Evolutionary Algorithm.- Diversity and Neuro-Ensemble.- Unsupervised Niche Clustering: Discovering an Unknown Number of Clusters in Noisy Data Sets.- Evolutionary Computation in Intelligent Network Management.- Genetic Programming in Data Mining for Drug Discovery.- Microarray Data Mining with Evolutionary Computation.- An Evolutionary Modularized Data Mining Mechanism for Financial Distress Forecasts.
• (source: Nielsen Book Data)

Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation).
(source: Nielsen Book Data)

• 1. Bio-inspired Algorithms
• 2. Data Classification Using Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron
• 3. Reconstruction of 3D Surfaces Using RBF Adjusted with PSO
• 4. Soft Computing Applications in Robot Vision
• 5. Soft Computing Applications inMobile Robotics
• 6. Particle Swarm Optimization to Improve Neural Identifiers for Discrete-time Unknown Nonlinear Systems
• 7. Bio-inspired Algorithms to Improve Neural Controllers for Discrete-time Unknown Nonlinear System
• 8. Final Remarks.
• (source: Nielsen Book Data)

Bio-inspired Algorithms for Engineering builds a bridge between the proposed bio-inspired algorithms developed in the past few decades and their applications in real-life problems, not only in an academic context, but also in the real world. The book proposes novel algorithms to solve real-life, complex problems, combining well-known bio-inspired algorithms with new concepts, including both rigorous analyses and unique applications. It covers both theoretical and practical methodologies, allowing readers to learn more about the implementation of bio-inspired algorithms. This book is a useful resource for both academic and industrial engineers working on artificial intelligence, robotics, machine learning, vision, classification, pattern recognition, identification and control.
(source: Nielsen Book Data)

• Linking Entropy to Estimation of Distribution Algorithms.- Entropy-based Convergence Measurement in Discrete Estimation of Distribution Algorithms.- Real-coded Bayesian Optimization Algorithm.- The CMA Evolution Strategy: A Comparing Review.- Estimation of Distribution Programming: EDA-based Approach to Program Generation.- Multi-objective Optimization with the Naive
• ID A.- A Parallel Island Model for Estimation of Distribution Algorithms.- GA-EDA: A New Hybrid Cooperative Search Evolutionary Algorithm.- Bayesian Classifiers in Optimization: An EDA-like Approach.- Feature Ranking Using an EDA-based Wrapper Approach.- Learning Linguistic Fuzzy Rules by Using Estimation of Distribution Algorithms as the Search Engine in the COR Methodology.- Estimation of Distribution Algorithm with 2-opt Local Search for the Quadratic Assignment Problem.
• (source: Nielsen Book Data)

Estimation of Distribution Algorithms (EDAs) are a set of algorithms in the Evolutionary Computation (EC) field characterized by the use of explicit probability distributions in optimization. Contrarily to other EC techniques such as the broadly known Genetic Algorithms (GAs) in EDAs, the crossover and mutation operators are substituted by the sampling of a distribution previously learnt from the selected individuals. EDAs have experienced a high development that has transformed them into an established discipline within the EC field. This book attracts the interest of new researchers in the EC field as well as in other optimization disciplines, and that it becomes a reference for all of us working on this topic. The twelve chapters of this book can be divided into those that endeavor to set a sound theoretical basis for EDAs, those that broaden the methodology of EDAs and finally those that have an applied objective.
(source: Nielsen Book Data)

• Part I Models & Theories.- Parallel BMDA with Probability Model Migration.- Linkages Detection in Histogram-based Estimation of Distribution Algorithm.- Linkage in Island Models.- Real-coded ECGA for Solving Decomposable Real-Valued Optimization Problems.- Linkage Learning Accuracy in the Bayesian Optimization Algorithm.- The Impact of Exact Probabilistic Learning Algorithms in EDAs based on Bayesian Networks.- Linkage Learning in Estimation of Distribution Algorithms.- Part II Operators & Frameworks.- Parallel GEAs with Linkage Analysis over Grid.- Identification and Exploitation of Linkage by Means of Alternative Splicing.- A Clustering-based Approach for Linkage Learning Applied to Multimodal Optimization.- Studying the Effects of Dual Coding on the Adaptation of Representation for Linkage in Evolutionary Algorithms.- Symbiotic Evolution to avoid Linkage Problem.- EpiSwarm, A Swarm-based System for Investigating Genetic Epistasis.- Real-Coded Extended Compact Genetic Algorithm based on Mixtures of Models.- Part III Applications.- Genetic Algorithms for the Airport Gate Assignment: Linkage, Representation and Uniform Crossover.- A Decomposed Approach for the Minimum Interference Frequency Assignment.- Set Representation and Multi-parent Learning within an Evolutionary Algorithm for Optimal Design of Trusses.- A Network Design Problem by a GA with Linkage Identification and Recombination for Overlapping Building Blocks.- Knowledge-based Evolutionary Linkage in MEMS Design Synthesis.
• (source: Nielsen Book Data)

In recent years, the issue of linkage in GEAs has garnered greater attention and recognition from researchers. Conventional approaches that rely much on ad hoc tweaking of parameters to control the search by balancing the level of exploitation and exploration are grossly inadequate. As shown in the work reported here, such parameters tweaking based approaches have their limits; they can be easily "fooled" by cases of triviality or peculiarity of the class of problems that the algorithms are designed to handle. Furthermore, these approaches are usually blind to the interactions between the decision variables, thereby disrupting the partial solutions that are being built up along the way.
(source: Nielsen Book Data)

• Intro
• Foreword
• Preface
• Organization of the Chapters
• Acknowledgments
• Contents
• Contributors
• Part I Methodology
• 1 SOMA
• Self-organizing Migrating Algorithm
• Abstract
• 1 Introduction
• 2 Historical Background and Algorithm Classification
• 2.1 SOMA in the Context of Selected Evolutionary Algorithms
• 3 SOMA Applicability
• 4 SOMA Principles and Control Parameters
• 5 SOMA Strategies
• 5.1 SOMA Parameters
• 5.2 Standard Evolutionary Operations in SOMA
• 5.2.1 Population
• 5.2.2 Mutations
• 5.2.3 Crossover
• 5.2.4 Constraint Handling

• 5.2.5 Boundary Constraints
• 5.2.6 Constraint Functions
• 5.2.7 Handling of Integer and Discrete Variables
• 6 Parameter Dependence
• 7 SOMA and Cost Function Evaluations
• 8 Selected SOMA Applications
• 9 SOMA in Computer Games
• 10 SOMA and Interdisciplinary Research
• 11 Conclusion
• Acknowledgments
• References
• 2 DSOMA
• Discrete Self Organising Migrating Algorithm
• Abstract
• 1 Introduction
• 2 Discrete Self-organising Migrating Algorithm
• 3 Initialisation
• 4 Creating Jump Sequences
• 5 Constructing Trial Individuals
• 6 Repairing Trial Individuals

• 7 Population Update
• 8 Iteration
• 9 Migrations
• 10 2 Opt Local Search
• 11 Conclusion
• Acknowledgments
• References
• Part II Implementation
• 3 SOMA and Strange Dynamics
• Abstract
• 1 Introduction
• 2 SOMA and Chaos
• 2.1 Chaos Synthesis
• 2.2 Chaos Control
• 2.3 Chaos Identification
• 2.4 SOMA Powered by Pseudorandom, Chaos and Deterministic Dynamics
• 3 SOMA and Fractal Geometry
• 4 SOMA Dynamics as a Complex Networks
• 5 Conclusion
• Acknowledgment
• References
• 4 Multi-objective Self-organizing Migrating Algorithm
• Abstract

• 1 Introduction to Multi-objective Optimization
• 2 MOSOMA
• 2.1 Controlling Parameters
• 2.2 Migration of Agents
• 2.3 Final Non-dominated Set Choice
• 3 Appendix I
• Evaluation Metrics
• 4 Appendix II
• Benchmark Problems
• Acknowledgements
• References
• 5 Multi-objective Design of EM Components
• Abstract
• 1 Design of EM Components
• 1.1 Yagi-Uda Antenna Design
• 1.2 Dielectric Layered Filter Design
• 1.3 Adaptive Beamforming in Time Domain
• Acknowledgements
• References
• 6 Utilization of Parallel Computing for Discrete Self-organizing Migration Algorithm
• Abstract

• 1 Introduction
• 2 Levels of Parallelization
• 3 Hardware and Software Options for Parallelization
• 3.1 OpenMP
• 3.2 Message Passing Interface
• 3.2.1 Brief Introduction into Kaira
• 3.3 GPU Computing with CUDA
• 4 Parallelization of DSOMA
• 4.1 OpenMP Implementation of DSOMA
• 4.2 Distributed Island Model Implementation of DSOMA
• 4.3 GPU Implementation
• 4.3.1 Data Storage, Transfers and Alignment
• 4.3.2 Data Level Prallelism
• 4.3.3 Single Thread Computation
• 4.3.4 Block/Warp Computation
• 5 Experiments
• 5.1 OpenMP Experiments
• 5.2 CUDA Experiments

This book brings together the current state of-the-art research in Self Organizing Migrating Algorithm (SOMA) as a novel population-based evolutionary algorithm, modeled on the predator-prey relationship, by its leading practitioners. As the first ever book on SOMA, this book is geared towards graduate students, academics and researchers, who are looking for a good optimization algorithm for their applications. This book presents the methodology of SOMA, covering both the real and discrete domains, and its various implementations in different research areas. The easy-to-follow and implement methodology used in the book will make it easier for a reader to implement, modify and utilize SOMA.

• On the Foundations and the Applications of Evolutionary Computing / Pierre Del Moral, Alexandru-Adrian Tantar and Emilia Tantar
• Incorporating Regular Vines in Estimation of Distribution Algorithms / Rogelio Salinas-Gutiérrez, Arturo Hernández-Aguirre and Enrique R. Villa-Diharce
• The Gaussian Polytree EDA with Copula Functions and Mutations / Ignacio Segovia Domínguez, Arturo Hernández Aguirre and Enrique Villa Diharce
• On Quality Indicators for Black-Box Level Set Approximation / Michael T.M. Emmerich, André H. Deutz and Johannes W. Kruisselbrink
• Set Oriented Methods for the Numerical Treatment of Multiobjective Optimization Problems / Oliver Schütze, Katrin Witting, Sina Ober-Blöbaum and Michael Dellnitz
• A Complex-Networks View of Hard Combinatorial Search Spaces / Marco Tomassini and Fabio Daolio
• Cooperative Coevolution for Agrifood Process Modeling / Olivier Barrière, Evelyne Lutton, Pierre-Henri Wuillemin, Cédric Baudrit and Mariette Sicard, et al.
• Hybridizing cGAs with PSO-like Mutation / E. Alba and A. Villagra
• On Gradient-Based Local Search to Hybridize Multi-objective Evolutionary Algorithms / Adriana Lara, Oliver Schütze and Carlos A. Coello Coello
• On the Integration of Theoretical Single-Objective Scheduling Results for Multi-objective Problems / Christian Grimme, Markus Kemmerling and Joachim Lepping
• Analysing the Robustness of Multiobjectivisation Approaches Applied to Large Scale Optimisation Problems / Carlos Segura, Eduardo Segredo and Coromoto León
• A Comparative Study of Heuristic Conversion Algorithms, Genetic Programming and Return Predictability on the German Market / Esther Mohr, Günter Schmidt and Sebastian Jansen.

The aim of this book is to provide a strong theoretical support for understanding and analyzing the behavior of evolutionary algorithms, as well as for creating a bridge between probability, set-oriented numerics and evolutionary computation. The volume encloses a collection of contributions that were presented at the EVOLVE 2011 international workshop, held in Luxembourg, May 25-27, 2011, coming from invited speakers and also from selected regular submissions. The aim of EVOLVE is to unify the perspectives offered by probability, set oriented numerics and evolutionary computation. EVOLVE focuses on challenging aspects that arise at the passage from theory to new paradigms and practice, elaborating on the foundations of evolutionary algorithms and theory-inspired methods merged with cutting-edge techniques that ensure performance guarantee factors. EVOLVE is also intended to foster a growing interest for robust and efficient methods with a sound theoretical background. The chapters enclose challenging theoretical findings, concrete optimization problems as well as new perspectives. By gathering contributions from researchers with different backgrounds, the book is expected to set the basis for a unified view and vocabulary where theoretical advancements may echo in different domains.
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• Index Fund Optimization Using Genetic Algorithm and Scatter Diagram Based on Coefficients of Determination.- Mining Bayesian Networks from Direct Marketing Databases with Missing Values.- Fuzzy Local Currency based on Social Network Analysis for Promoting Community Businesses.- Evolving Failure Resilience in Scale-Free Networks
• Evolving Networks with Enhanced Linear Stability Properties.- Effectiveness of Close-loop Congestion Controls for DDoS Attacks.- Priority-based Genetic Algorithm for Shortest Path Routing Problem in OSPF.- Evolutionary Network Design by Multiobjective Hybrid Genetic Algorithm.- Hybrid Genetic Algorithm for Designing Logistics Network, VRP and AGV Problems.- Multiobjective Genetic Algorithm for Bicriteria Network Design Problems.- Use of Serendipity Power for Discoveries and Inventions.- Evolution of Retinal Blood Vessel Segmentation Methodology using Wavelet Transforms for Assessment of Diabetic Retinopathy.- Multistage-based Genetic Algorithm for Flexible Job-shop Scheduling Problems.- Implementation of Parallel Genetic Algorithms on Graphics Processing Units.
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Artificial evolutionary systems are computer systems, inspired by ideas from natural evolution and related phenomena. The field has a long history, dating back to the earliest days of computer science, but it has only become an established scientific and engineering discipline since the 1990s, with packages for the commonest form, genetic algorithms, now widely available. Researchers in the Asia-Pacific region have participated strongly in the development of evolutionary systems, with a particular emphasis on the evolution of intelligent solutions to highly complex problems. The Asia-Pacific Symposia on Intelligent and Evolutionary Systems have been an important contributor to this growth in impact, since 1997 providing an annual forum for exchange and dissemination of ideas. Participants come primarily from East Asia and the Western Pacific, but contributions are welcomed from around the World. This volume features a selection of fourteen of the best papers from recent APSIES. They illustrate the breadth of research in the region, with applications ranging from business to medicine, from network optimization to the promotion of innovation.
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• Parallel Evolutionary Optimization.- A Model for Parallel Operators in Genetic Algorithms.- Parallel Evolutionary Multiobjective Optimization.- Parallel Hardware for Genetic Algorithms.- A Reconfigurable Parallel Hardware for Genetic Algorithms.- Reconfigurable Computing and Parallelism for Implementing and Accelerating Evolutionary Algorithms.- Distributed Evolutionary Computation.- Performance of Distributed GAs on DNA Fragment Assembly.- On Parallel Evolutionary Algorithms on the Computational Grid.- Parallel Evolutionary Algorithms on Consumer-Level Graphics Processing Unit.- Parallel Particle Swarm Optimization.- Intelligent Parallel Particle Swarm Optimization Algorithms.- Parallel Ant Colony Optimization for 3D Protein Structure Prediction using the HP Lattice Model.
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This book focuses on the aspects related to the parallelization of evolutionary computations, such as parallel genetic operators, parallel fitness evaluation, distributed genetic algorithms, and parallel hardware implementations, as well as on their impact on several applications. It offers a wide spectrum of sample works developed in leading research about parallel implementations of efficient techniques at the heart of computational intelligence.
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• Bio-Inspired Computational Intelligence for the Hardware of Adaptive Systems.- Advanced Hardware Implementation of the Computational Intelligence and Intelligent Technologies.- Bio-Inspired Analogue and Digital Circuits and Their Applications.
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Evolvable Hardware (EHW) has emerged as a sub-domain of artificial evolution represented by a design methodology (consortium of methods) involving the application of Evolutionary Algorithms (EA) to the synthesis of digital and analogue electronic circuits and systems. Nevertheless, the most benefit for the society and indeed most revolutionizing application of EA is its hardware implementation leading to the EHW. These new EA based methodologies led to a new type of machines that is evolved to attain a desired behaviour, which means they have a behavioural computational intelligence. EHW is a special case of the adaptive hardware, namely being strongly related to the Adaptive Systems (AS) and the Adaptive Hardware (AH). The book presents a careful selection of the field that very well reflects the breadth of this high technology and its terminology and applications in context of the AS/AH. The harmonious symbiosis of the engineering approach and the accurate scientific methodology features the aspects of highly relevant and practical design principles governing the development of EHW and its connections with AS/AH. This book is both attractive and useful for everybody interested in the design and analysis of EHW in context of AS/AH and implementation of real time adaptive hardware hybrid intelligent systems.
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• Introduction.- RelatedWork and Background.- Theoretical Analysis of Differential Evolution.- Parameter Adaptive Differential Evolution.- Surrogate Model-based Differential Evolution.- Adaptive Multi-Objective Differential Evolution.- Application to Winner Determination Problems in Combinatorial Auctions.- Application to Flight Planning in Air Traffic Control Systems.- Application to the TPM Optimization in Credit Decision Making.- Conclusions and Future Work.
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Optimization problems are ubiquitous in academic research and real-world applications wherever such resources as space, time and cost are limited. Researchers and practitioners need to solve problems fundamental to their daily work which, however, may show a variety of challenging characteristics such as discontinuity, nonlinearity, nonconvexity, and multimodality. It is expected that solving a complex optimization problem itself should easy to use, reliable and efficient to achieve satisfactory solutions. Differential evolution is a recent branch of evolutionary algorithms that is capable of addressing a wide set of complex optimization problems in a relatively uniform and conceptually simple manner. For better performance, the control parameters of differential evolution need to be set appropriately as they have different effects on evolutionary search behaviours for various problems or at different optimization stages of a single problem. The fundamental theme of the book is theoretical study of differential evolution and algorithmic analysis of parameter adaptive schemes. Topics covered in this book include: Theoretical analysis of differential evolution and its control parameters Algorithmic design and comparative analysis of parameter adaptive schemes Scalability analysis of adaptive differential evolution Adaptive differential evolution for multi-objective optimization Incorporation of surrogate model for computationally expensive optimization Application to winner determination in combinatorial auctions of E-Commerce Application to flight route planning in Air Traffic Management Application to transition probability matrix optimization in credit-decision making.

• A Novel Ant Colony Optimization Strategy for the Quantum Circuit Compilation Problem.- Hybridization of Racing Methods with Evolutionary Operators for Simulation Optimization of Traffic Lights Programs.- Decomposition-based Multi-objective Landscape Features and Automated Algorithm Selection.- MATE: A Model-Based Algorithm Tuning Engine.- An Improvement Heuristic Based on Variable Neighborhood Search for a Dynamic Orienteering Problem.- Runtime Analysis of the (mu+1)-EA on the Dynamic BinVal Function.- Tabu-Driven Quantum Neighborhood Samplers.- On Hybrid Heuristics for Steiner Trees on the Plane with Obstacles.- Flowshop NEH-Based Heuristic Recommendation.- Stagnation Detection with Randomized Local Search.- An Artificial Immune System for Black Box Test Case Selection.- Symmetry Breaking for Voting Mechanisms.- A Heuristic Algorithm for School Bus Routing with Bus Stop Selection.- Hybrid Heuristic and Metaheuristic for Solving Electric Vehicle Charging Scheduling Problem.
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This book constitutes the refereed proceedings of the 21st European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2021, held as part of Evo*2021, as Virtual Event, in April 2021, co-located with the Evo*2021 events: EvoMUSART, EvoApplications, and EuroGP. The 14 revised full papers presented in this book were carefully reviewed and selected from 42 submissions. They cover a wide spectrum of topics, ranging from the foundations of evolutionary algorithms and other search heuristics to their accurate design and application to combinatorial optimization problems. Fundamental and methodological aspects deal with runtime analysis, the structural properties of fitness landscapes, the study of core components of metaheuristics, the clever design of their search principles, and their careful selection and configuration. Applications cover problem domains such as scheduling, routing, search-based software engineering and general graph problems. The range of topics covered in this volume reflects the current state of research in the fields of evolutionary computation and combinatorial optimization.
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• Optimizing Prices and Periods in Time-of-use Electricity Tariff Design Using Bilevel Programming.- An Algebraic Approach for the Search Space of Permutations with Repetition.- A Comparison of Genetic Representations for Multi-Objective Shortest Path Problems on Multigraphs.- The Univariate Marginal Distribution Algorithm Copes well with Deception and Epistasis.- A Beam Search Approach to the Traveling Tournament Problem.- Cooperative Parallel SAT Local Search with Path Relinking.- Dynamic Compartmental Models for Large Multi-Objective Landscapes and Performance Estimation.- Fitness Landscape Analysis of Automated Machine Learning Search Spaces.- On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D.- A Grouping Genetic Algorithm for Multi Depot Pickup and Delivery Problems with Time Windows and Heterogeneous Vehicle Fleets.- MILPIBEA: Algorithm for Multi-Objective Features Selection in (Evolving) Software Product Lines.- A Group Genetic Algorithm for Resource Allocation in Container-Based Clouds.- The Local Optima Level in Chemotherapy Schedule Optimisation.- Genetic Programming with Adaptive Search Based on the Frequency of Features for Dynamic Flexible Job Shop Scheduling.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 20th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2020, held as part of Evo*2020, in Seville, Spain, in April 2020, co-located with the Evo*2020 events EuroGP, EvoMUSART and EvoApplications. The 14 full papers presented in this book were carefully reviewed and selected from 37 submissions. The papers cover a wide spectrum of topics, ranging from the foundations of evolutionary computation algorithms and other search heuristics, to their accurate design and application to combinatorial optimization problems.
(source: Nielsen Book Data)

• Evolutionary Image Analysis and Signal Processing.- Texture Image Segmentation Using an Interactive Evolutionary Approach.- Detecting Scale-Invariant Regions Using Evolved Image Operators.- Online Evolvable Pattern Recognition Hardware.- A Variant Program Structure in Tree-Based Genetic Programming for Multiclass Object Classification.- Genetic Programming for Generative Learning and Recognition of Hand-Drawn Shapes.- Optimizing a Medical Image Analysis System Using Mixed-Integer Evolution Strategies.- Memetic Differential Evolution Frameworks in Filter Design for Defect Detection in Paper Production.- Fast Genetic Scan Matching in Mobile Robotics.- Distributed Differential Evolution for the Registration of Satellite and Multimodal Medical Imagery.- Euclidean Distance Fit of Conics Using Differential Evolution.- An Evolutionary FIR Filter Design Method.
• (source: Nielsen Book Data)

The publication of this book on evolutionaryImage Analysis and Signal P- cessing (IASP) has two main goals. The ?rst, occasional one is to celebrate the 10th edition of EvoIASP, the workshop which has been the only event speci?cally dedicated to this topic since 1999. The second, more important one is to give an overview of the opportunities o?ered by Evolutionary C- putation (EC) techniques to computer vision, pattern recognition, and image and signal processing. It is not possible to celebrate EvoIASP properly without ?rst ackno- edging EvoNET, the EU-funded network of excellence, which has made it possible for Europe to build a strong European research community on EC. Thanks to the success of the ?rst, pioneering event organized by EvoNET, held in 1998 in Paris, it was possible to realize that not only was EC a f- tile ground for basic research but also there were several application ?elds to which EC techniques could o?er a valuable contribution. That was how the ideaofcreatingasingleevent, EvoWorkshops, outofacollectionofworkshops dedicated to applications of EC, was born. Amongst the possible application ?elds for EC, IASP was selected almost accidentally, due to the occasional presence, within EvoNET, of less than a handful of researchers who were interested in it. I would lie if I stated that the event was a great success since its very start, but it was successful enough to survive healthily for a couple of years, before reaching its present size, relevance, and popularity.
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• EvoCOMNET Contributions.- Web Application Security through Gene Expression Programming.- Location Discovery in Wireless Sensor Networks Using a Two-Stage Simulated Annealing.- Wireless Communications for Distributed Navigation in Robot Swarms.- An Evolutionary Algorithm for Survivable Virtual Topology Mapping in Optical WDM Networks.- Extremal Optimization as a Viable Means for Mapping in Grids.- Swarm Intelligence Inspired Multicast Routing: An Ant Colony Optimization Approach.- A Framework for Evolutionary Peer-to-Peer Overlay Schemes.- Multiuser Scheduling in HSDPA with Particle Swarm Optimization.- Efficient Signal Processing and Anomaly Detection in Wireless Sensor Networks.- Peer-to-Peer Optimization in Large Unreliable Networks with Branch-and-Bound and Particle Swarms.- Evolving High-Speed, Easy-to-Understand Network Intrusion Detection Rules with Genetic Programming.- Soft Computing Techniques for Internet Backbone Traffic Anomaly Detection.- Testing Detector Parameterization Using Evolutionary Exploit Generation.- Ant Routing with Distributed Geographical Localization of Knowledge in Ad-Hoc Networks.- Discrete Particle Swarm Optimization for Multiple Destination Routing Problems.- EvoENVIRONMENT Contributions.- Combining Back-Propagation and Genetic Algorithms to Train Neural Networks for Ambient Temperature Modeling in Italy.- Estimating the Concentration of Nitrates in Water Samples Using PSO and VNS Approaches.- Optimal Irrigation Scheduling with Evolutionary Algorithms.- Adaptive Land-Use Management in Dynamic Ecological System.- EvoFIN Contributions.- Evolutionary Money Management.- Prediction of Interday Stock Prices Using Developmental and Linear Genetic Programming.- An Introduction to Natural Computing in Finance.- Evolutionary Approaches for Estimating a Coupled Markov Chain Model for Credit Portfolio Risk Management.- Knowledge Patterns in Evolutionary Decision Support Systems for Financial Time Series Analysis.- Predicting Turning Points in Financial Markets with Fuzzy-Evolutionary and Neuro-Evolutionary Modeling.- Comparison of Multi-agent Co-operative Co-evolutionary and Evolutionary Algorithms for Multi-objective Portfolio Optimization.- Dynamic High Frequency Trading: A Neuro-Evolutionary Approach.- EvoGAMES Contributions.- Decay of Invincible Clusters of Cooperators in the Evolutionary Prisoner's Dilemma Game.- Evolutionary Equilibria Detection in Non-cooperative Games.- Coevolution of Competing Agent Species in a Game-Like Environment.- Simulation Minus One Makes a Game.- Evolving Simple Art-Based Games.- Swarming for Games: Immersion in Complex Systems.- Fitness Diversity Parallel Evolution Algorithms in the Turtle Race Game.- Evolving Strategies for Non-player Characters in Unsteady Environments.- Grid Coevolution for Adaptive Simulations: Application to the Building of Opening Books in the Game of Go.- Evolving Teams of Cooperating Agents for Real-Time Strategy Game.- EvoHOT Contributions.- Design Optimization of Radio Frequency Discrete Tuning Varactors.- An Evolutionary Path Planner for Multiple Robot Arms.- Evolutionary Optimization of Number of Gates in PLA Circuits Implemented in VLSI Circuits.- Particle Swarm Optimisation as a Hardware-Oriented Meta-heuristic for Image Analysis.- EvoIASP Contributions.- A Novel GP Approach to Synthesize Vegetation Indices for Soil Erosion Assessment.- Flies Open a Door to SLAM.- Genetic Image Network for Image Classification.- Multiple Network CGP for the Classification of Mammograms.- Evolving Local Descriptor Operators through Genetic Programming.- Evolutionary Optimization for Plasmon-Assisted Lithography.- An Improved Multi-objective Technique for Fuzzy Clustering with Application to IRS Image Segmentation.- EvoINTERACTION Contributions.- Interactive Evolutionary Evaluation through Spatial Partitioning of Fitness Zones.- Fractal Evolver: Interactive Evolutionary Design of Fractals with Grid Computing.- Humorized Computational Intelligence towards User-Adapted Systems with a Sense of Humor.- Innovative Chance Discovery - Extracting Customers' Innovative Concept.- EvoMUSART Contributions.- Evolving Approximate Image Filters.- On the Role of Temporary Storage in Interactive Evolution.- Habitat: Engineering in a Simulated Audible Ecosystem.- The Evolution of Evolutionary Software: Intelligent Rhythm Generation in Kinetic Engine.- Filterscape: Energy Recycling in a Creative Ecosystem.- Evolved Ricochet Compositions.- Life's What You Make: Niche Construction and Evolutionary Art.- Global Expectation-Violation as Fitness Function in Evolutionary Composition.- Composing Using Heterogeneous Cellular Automata.- On the Socialization of Evolutionary Art.- An Evolutionary Music Composer Algorithm for Bass Harmonization.- Generation of Pop-Rock Chord Sequences Using Genetic Algorithms and Variable Neighborhood Search.- Elevated Pitch: Automated Grammatical Evolution of Short Compositions.- A GA-Based Control Strategy to Create Music with a Chaotic System.- Teaching Evolutionary Design Systems by Extending "Context Free".- Artificial Nature: Immersive World Making.- Evolving Indirectly Represented Melodies with Corpus-Based Fitness Evaluation.- Hearing Thinking.- EvoNUM Contributions.- Memetic Variation Local Search vs. Life-Time Learning in Electrical Impedance Tomography.- Estimating HMM Parameters Using Particle Swarm Optimisation.- Modeling Pheromone Dispensers Using Genetic Programming.- NK Landscapes Difficulty and Negative Slope Coefficient: How Sampling Influences the Results.- On the Parallel Speed-Up of Estimation of Multivariate Normal Algorithm and Evolution Strategies.- Adaptability of Algorithms for Real-Valued Optimization.- A Stigmergy-Based Algorithm for Continuous Optimization Tested on Real-Life-Like Environment.- Stochastic Local Search Techniques with Unimodal Continuous Distributions: A Survey.- Evolutionary Optimization Guided by Entropy-Based Discretization.- EvoSTOC Contributions.- The Influence of Population and Memory Sizes on the Evolutionary Algorithm's Performance for Dynamic Environments.- Differential Evolution with Noise Analyzer.- An Immune System Based Genetic Algorithm Using Permutation-Based Dualism for Dynamic Traveling Salesman Problems.- Dynamic Time-Linkage Problems Revisited.- The Dynamic Knapsack Problem Revisited: A New Benchmark Problem for Dynamic Combinatorial Optimisation.- Impact of Frequency and Severity on Non-Stationary Optimization Problems.- A Critical Look at Dynamic Multi-dimensional Knapsack Problem Generation.- EvoTRANSLOG Contributions.- Evolutionary Freight Transportation Planning.- An Effective Evolutionary Algorithm for the Cumulative Capacitated Vehicle Routing Problem.- A Corridor Method-Based Algorithm for the Pre-marshalling Problem.- Comparison of Metaheuristic Approaches for Multi-objective Simulation-Based Optimization in Supply Chain Inventory Management.- Heuristic Algorithm for Coordination in Public Transport under Disruptions.- Optimal Co-evolutionary Strategies for the Competitive Maritime Network Design Problem.
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The year 2009 celebrates the bicentenary of Darwin's birth and the 150th - niversary of the publication of his seminal work, On the Origin of Species.If this makes 2009 a special year for the research community working in biology and evolution, the ?eld of evolutionary computation (EC) also shares the same excitement. EC techniques are e?cient, nature-inspired planning and optimi- tion methods based on the principles of natural evolution and genetics. Due to their e?ciency and simple underlying principles, these methods can be used in the context of problem solving, optimization, and machine learning. A large and ever-increasing number of researchers and professionals make use of EC te- niques in various application domains. ThisvolumepresentsacarefulselectionofrelevantECapplicationscombi ned with a thorough examination of the techniques used in EC. The papers in the volume illustrate the current state of the art in the application of EC and can help and inspire researchers and professionals to develop e?cient EC methods for design and problem solving.
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• An introduction to evolutionary computation
• evolutionary algorithms as search algorithms
• theoretical analysis of evolutionary algorithms
• advanced search operators in evolutionary algorithms
• parallel evolutionary algorithms
• a comparison of simulated annealing and an evolutionary algorithm on traveling salesman problems
• power system design and management by evolutionary algorithms
• telecommunications network design and management by evolutionary algorithms
• an optimization tool based on evolutionary algorithms
• the evolution of artificial neural network architectures
• an experimental study of generalization in evolutionary learning
• an evolutionary approach to the N-person prisoner's dilemma game
• automated design and generalisation of heuristics
• high-order credit assignment in classifier systems.
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Evolutionary computation is the study of computational systems which use ideas and get inspiration from natural evolution and adaptation. This book is devoted to the theory and application of evolutionary computation. It is a self-contained volume which covers both introductory material and selected advanced topics. The book can roughly be divided into two major parts: the introductory one and the one on selected advanced topics. Each part consists of several chapters which present an in-depth discussion of selected topics. A strong connection is established between evolutionary algorithms and traditional search algorithms. This connection enables us to incorporate ideas in more established fields into evolutionary algorithms. The book is aimed at a wide range of readers. It does not require previous exposure to the field since introductory material is included. It will be of interest to anyone who is interested in adaptive optimization and learning. People in computer science, artificial intelligence, operations research, and various engineering fields will find it particularly interesting.
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• Methodology.- Memetic Algorithms in Planning, Scheduling, and Timetabling.- Landscapes, Embedded Paths and Evolutionary Scheduling.- Classical and Non-Classical Models of Production Scheduling.- Scheduling of Flow-Shop, Job-Shop, and Combined Scheduling Problems using MOEAs with Fixed and Variable Length Chromosomes.- Designing Dispatching Rules to Minimize Total Tardiness.- A Robust Meta-Hyper-Heuristic Approach to Hybrid Flow-Shop Scheduling.- Hybrid Particle Swarm Optimizers in the Single Machine Scheduling Problem: An Experimental Study.- An Evolutionary Approach for Solving the Multi-Objective Job-Shop Scheduling Problem.- Timetabling.- Multi-Objective Evolutionary Algorithm for University Class Timetabling Problem.- Metaheuristics for University Course Timetabling.- Energy Applications.- Optimum Oil Production Planning using an Evolutionary Approach.- A Hybrid Evolutionary Algorithm for Service Restoration in Power Distribution Systems.- Particle Swarm Optimisation for Operational Planning: Unit Commitment and Economic Dispatch.- Evolutionary Generator Maintenance Scheduling in Power Systems.- Networks.- Evolvable Fuzzy Scheduling Scheme for Multiple-ChannelPacket Switching Network.- A Multi-Objective Evolutionary Algorithm for Channel Routing Problems.- Transport.- Simultaneous Planning and Scheduling for Multi-Autonomous Vehicles.- Scheduling Production and Distribution of Rapidly Perishable Materials with Hybrid GA's.- A Scenario-based Evolutionary Scheduling Approach for Assessing Future Supply Chain Fleet Capabilities.- Business.- Evolutionary Optimization of Business Process Designs.- Using a Large Set of Low Level Heuristics in a Hyperheuristic Approach to Personnel Scheduling.- A Genetic-Algorithm-Based Reconfigurable Scheduler.- Evolutionary Algorithm for an Inventory Location Problem.
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Evolutionary scheduling is a vital research domain at the interface of artificial intelligence and operational research. This edited book gives an overview of many of the current developments in the large and growing field of evolutionary scheduling. It demonstrates the applicability of evolutionary computational techniques to solve scheduling problems, not only to small-scale test problems, but also fully-fledged real-world problems.
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• I: Foundations of Evolutionary Computation.- Evolutionary Algorithms.- Self-Adaptation.- II: Self-Adaptive Operators.- Biased Mutation for Evolution Strategies.- Self-Adaptive Inversion Mutation.- Self-Adaptive Crossover.- III: Constraint Handling.- Constraint Handling Heuristics for Evolution Strategies.- IV: Summary.- Summary and Conclusion.- V: Appendix.- Continuous Benchmark Functions.- Discrete Benchmark Functions.
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Evolutionary algorithms are successful biologically inspired meta-heuristics. Their success depends on adequate parameter settings. The question arises: how can evolutionary algorithms learn parameters automatically during the optimization? Evolution strategies gave an answer decades ago: self-adaptation. Their self-adaptive mutation control turned out to be exceptionally successful. But nevertheless self-adaptation has not achieved the attention it deserves. This book introduces various types of self-adaptive parameters for evolutionary computation. Biased mutation for evolution strategies is useful for constrained search spaces. Self-adaptive inversion mutation accelerates the search on combinatorial TSP-like problems. After the analysis of self-adaptive crossover operators the book concentrates on premature convergence of self-adaptive mutation control at the constraint boundary. Besides extensive experiments, statistical tests and some theoretical investigations enrich the analysis of the proposed concepts.
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• Swarm Intelligence and Its Applications.- Operational Technologies and Networked Multimedia Applications.- Wearable Computing and Intelligent Data Hiding.- Image Processing and Intelligent Applications.- Intelligent Multimedia Tools and Applications.- Technologies for Next-Generation Network Environments.- Recent Progress in Computational Electromagnetic Dynamics.- Future Cyber Security, Privacy and Forensics for Advanced systems.- Data Mining Techniques and its Applications.- Optimization Models in Deep Learning/Machine Learning.
• (source: Nielsen Book Data)

This book contains selected papers presented at ICGEC 2021, the 14th International Conference on Genetic and Evolutionary Computing, held from October 21-23, 2021 in Jilin City, China. The conference was technically co-sponsored by Springer, Northeast Electric Power University Fujian University of Technology, Shandong University of Science and Technology, and Western Norway University of Applied Sciences. It is intended as an international forum for the researchers and professionals in all areas of genetic and evolutionary computing. And the readers may learn the up-to-date techniques of the mentioned topics, including swarm intelligence, artificial intelligence, information hiding and data mining techniques, which can help them to bring new ideas or apply the designed approaches from the collected papers to their professional jobs.
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• Swarm Intelligence and Its Applications.- Operational Technologies and Networked Multimedia Applications.- Wearable Computing and Intelligent Data Hiding.- Image Processing and Intelligent Applications.- Intelligent Multimedia Tools and Applications.- Technologies for Next-Generation Network Environments.- Recent Progress in Computational Electromagnetic Dynamics.- Future Cyber Security, Privacy and Forensics for Advanced systems.- Data Mining Techniques and its Applications.
• (source: Nielsen Book Data)

This book gathers papers presented at the 13th International Conference on Genetic and Evolutionary Computing (ICGEC 2019), which was held in Qingdao, China, from 1st to 3rd, November 2019. Since it was established, in 2006, the ICGEC conference series has been devoted to new approaches with a focus on evolutionary computing. Today, it is a forum for the researchers and professionals in all areas of computational intelligence including evolutionary computing, machine learning, soft computing, data mining, multimedia and signal processing, swarm intelligence and security. The book appeals to policymakers, academics, educators, researchers in pedagogy and learning theory, school teachers, and other professionals in the learning industry, and further and continuing education. .
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• Ensemble Learning to Generate Virtual Data for Pattern Recognition and Its Possibility of Application to Strategy Inference in Table Tennis.- The rise of smart contract and its complex impacts on the digital ecosystem.- Automatic Collision Avoidance Using Deep Reinforcement Learning with Grid Sensor.- Two way allocation methods in SAVS for large depopulated area.- Proposal of mathematical model of prediction of human movement using position information.- Public transport smart card data analysis using tucker decomposition.- Estimating impressions for clothing, landscape, and indoor images using CNN.- Block-Based Neural Network High Speed Optimization.- An Improved Auto-encoder Based on 2-level Prioritized Experience Replay for High Dimension Skewed Data.- Multi-Task Learning Using Online Fine-Tuning Considering the Importance of Each Filter.- Visualizing Deep Q-Learning to Understanding Behavior of Swarm Robotic System.- A Study of Upper Tropospheric Circulations over the Northern Hemisphere Prediction Using Multivariate Features by ConvLSTM.- Learning localization skills of soccer robot using by simulated omni-vision camera.- Examination of analysis method of opinion distribution in News media transferred on Web.- Opinion distribution analysis of video media related to social life.- Quantitative consideration of spatio-temporal information on education and culture in Japan using telephone directory archive data.
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This book presents selected papers from the 23rd Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2019), which was held in Tottori, Japan, on December 6-8, 2019. Today, various types of intelligent system can be found everywhere. However, none of them can be developed or understood from only one perspective. As such, this book collects unique ways of thinking about intelligent systems. It discusses evolutionary and complex adaptive systems, which have been useful approaches for tackling intelligent systems. It also examines the recent developments in the field of artificial intelligence that are driving research on intelligent systems. Addressing topics related to intelligent transport systems, machine learning and neural networks, data science and decision analytics, evolutionary and nature-inspired computation, and agents and complex systems, this book is a valuable resource for researchers and practitioners wanting to develop or understand intelligent and evolutionary systems. .
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• Faster Result Retrieval from Health Care Product Sales Data Warehouse using Materialized Queries.- Feature Selection using Ant Colony Optimization and Weighted Visibility Graph.- From Generic to Custom: A Survey on Role of Machine Learning in Pharmacogenomics, Its Applications and Challenges.- Personalized Structure Balance Theory Based Movie Recommendation System.- Machine Learning Techniques for the Investigation of Phishing Websites.
• (source: Nielsen Book Data)

This book presents the proceedings of 8th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2020), which aims to bring together researchers, scientists, engineers and practitioners to share new ideas and experiences in the domain of intelligent computing theories with prospective applications to various engineering disciplines. The book is divided into two volumes: Evolution in Computational Intelligence (Volume 1) and Intelligent Data Engineering and Analytics (Volume 2). Covering a broad range of topics in computational intelligence, the book features papers on theoretical as well as practical aspects of areas such as ANN and genetic algorithms, computer interaction, intelligent control optimization, evolutionary computing, intelligent e-learning systems, machine learning, mobile computing, and multi-agent systems. As such, it is a valuable reference resource for postgraduate students in various engineering disciplines.
(source: Nielsen Book Data)

• Evolution of Analog Circuits
• Unconstrained Evolution of Analogue Computational "QR" Circuit with Oscillating Length Representation
• ISCLEs: Importance Sampled Circuit Learning Ensembles for Trustworthy Analog Circuit Topology Synthesis
• Evolution of Digital Circuits
• A Comparison of Evolvable Hardware Architectures for Classification Tasks
• Hardware Acceleration of an Immune Network Inspired Evolutionary Algorithm for Medical Diagnosis
• A Stepwise Dimension Reduction Approach to Evolutionary Design of Relative Large Combinational Logic Circuits
• Hardware-Software Codesign and Platforms for Adaptive Systems
• Evolutionary Graph Models with Dynamic Topologies on the Ubichip
• A Hardware-Software Design Framework for Distributed Cellular Computing
• Hardware/Software Co-synthesis of Distributed Embedded Systems Using Genetic Programming
• Self-Adaptive Networked Entities for Building Pervasive Computing Architectures
• Best Paper Award Competition
• Cellular Automata-Based Development of Combinational and Polymorphic Circuits: A Comparative Study
• Investigating the Suitability of FPAAs for Evolved Hardware Spiking Neural Networks
• The Segmental-Transmission-Line: Its Design and Prototype Evaluation
• On Evolutionary Synthesis of Linear Transforms in FPGA
• Evolutionary Robotics
• Towards Efficient Evolutionary Design of Autonomous Robots
• Indirect Online Evolution
• A Conceptual Framework for Adaptation in Industrial Robotic Systems
• Development
• A Developmental Gene Regulation Network for Constructing Electronic Circuits
• Discovery and Investigation of Inherent Scalability in Developmental Genomes
• Learning General Solutions through Multiple Evaluations during Development
• Real-World Applications
• Evolving MEMS Resonator Designs for Fabrication
• Self-Reconfigurable Mixed-Signal Integrated Circuits Architecture Comprising a Field Programmable Analog Array and a General Purpose Genetic Algorithm IP Core
• Evolutionary Networking
• Proposal for LDPC Code Design System Using Multi-Objective Optimization and FPGA-Based Emulation
• Scalability of a Novel Shifting Balance Theory-Based Optimization Algorithm: A Comparative Study on a Cluster-Based Wireless Sensor Network
• Evolutionary Design of Fault Tolerant Collective Communications
• Evolvable Artificial Neural Networks
• A Cellular Structure for Online Routing of Digital Spiking Neuron Axons and Dendrites on FPGAs
• Bio-inspired Event Coded Configurable Analog Circuit Block
• Dynamics of Firing Patterns in Evolvable Hierarchically Organized Neural Networks
• Transistor-Level Circuit Evolution
• Evolving Variability-Tolerant CMOS Designs
• Transistor-Level Evolution of Digital Circuits Using a Special Circuit Simulator
• Extended Posters
• Optimised State Assignment for FSMs Using Quantum Inspired Evolutionary Algorithm
• Evolvable Hardware: A Tool for Reverse Engineering of Biological Systems
• Coevolution of Neuro-developmental Programs That Play Checkers
• Hippocampus-Inspired Spiking Neural Network on FPGA
• Fault-Tolerant Memory Design and Partitioning Issues in Embryonics
• The Input Pattern Order Problem: Evolution of Combinatorial and Sequential Circuits in Hardware
• Neural Development on the Ubichip by Means of Dynamic Routing Mechanisms
• Short Posters
• The Perplexus Programming Framework: Combining Bio-inspiration and Agent-Oriented Programming for the Simulation of Large Scale Complex Systems
• Quantum Bio-inspired Vision Model on System-on-a-Chip (SoC)
• Evolutionary Meta Compilation: Evolving Programs Using Real World Engineering Tools
• Waveguide Synthesis by Genetic Algorithms with Multiple Crossover
• Parallel Grammatical Evolution for Circuit Optimization
• Self-organization of Bio-inspired Integrated Circuits
• Artificial Creativity in Linguistics Using Evolvable Fuzzy Neural Networks.

This book constitutes the refereed proceedings of the 8th International Conference on Evolvable Systems, ICES 2008, held in Prague, Czech Republic, in September 2008. The 28 revised full papers and 14 revised poster papers presented were carefully reviewed and selected from 52 submissions. The papers are organized in topical sections on evolution of analog circuits, evolution of digital circuits, hardware-software codesign and platforms for adaptive systems, evolutionary robotics, development, real-world applications, evolutionary networking, evolvable artificial neural networks, and transistor-level circuit evolution.
(source: Nielsen Book Data)

• Continuous Constrained Optimization with Dynamic Tolerance Using the COPSO Algorithm.- Boundary Search for Constrained Numerical Optimization Problems.- Solving Difficult Constrained Optimization Problems by the ? Constrained Differential Evolution with Gradient-Based Mutation.- Constrained Real-Parameter Optimization with ? -Self-Adaptive Differential Evolution.- Self-adaptive and Deterministic Parameter Control in Differential Evolution for Constrained Optimization.- An Adaptive Penalty Function for Handling Constraint in Multi-objective Evolutionary Optimization.- Infeasibility Driven Evolutionary Algorithm for Constrained Optimization.- On GA-AIS Hybrids for Constrained Optimization Problems in Engineering.- Constrained Optimization Based on Quadratic Approximations in Genetic Algorithms.- Constraint-Handling in Evolutionary Aerodynamic Design.- Handling Constraints in Global Optimization Using Artificial Immune Systems: A Survey.
• (source: Nielsen Book Data)

Evolutionary algorithms (EAs), as well as other bio-inspired heuristics, are widely usedto solvenumericaloptimizationproblems.However, intheir or- inal versions, they are limited to unconstrained search spaces i.e they do not include a mechanism to incorporate feasibility information into the ?tness function. On the other hand, real-world problems usually have constraints in their models. Therefore, a considerable amount of research has been d- icated to design and implement constraint-handling techniques. The use of (exterior) penalty functions is one of the most popular methods to deal with constrained search spaces when using EAs. However, other alternative me- ods have been proposed such as: special encodings and operators, decoders, the use of multiobjective concepts, among others. An e?cient and adequate constraint-handling technique is a key element in the design of competitive evolutionary algorithms to solve complex op- mization problems. In this way, this subject deserves special research e?orts. After asuccessfulspecialsessiononconstraint-handlingtechniquesusedin evolutionary algorithms within the Congress on Evolutionary Computation (CEC) in 2007, and motivated by the kind invitation made by Dr. Janusz Kacprzyk, I decided to edit a book, with the aim of putting together recent studies on constrained numerical optimization using evolutionary algorithms and other bio-inspired approaches. The intended audience for this book comprises graduate students, prac- tionersandresearchersinterestedonalternativetechniquestosolvenum erical optimization problems in presence of constraints.
(source: Nielsen Book Data)

• Agent Based Evolutionary Approach: An Introduction
• Multi-Agent Evolutionary Model for Global Numerical Optimization
• An Agent Based Evolutionary Approach for Nonlinear Optimization with Equality Constraints
• Multiagent-Based Approach for Risk Analysis in Mission Capability Planning
• Agent Based Evolutionary Dynamic Optimization
• Divide and Conquer in Coevolution: A Difficult Balancing Act
• Complex Emergent Behaviour from Evolutionary Spatial Animat Agents
• An Agent-Based Parallel Ant Algorithm with an Adaptive Migration Controller
• An Attempt to Stochastic Modeling of Memetic Systems
• Searching for the Effective Bidding Strategy Using Parameter Tuning in Genetic Algorithm
• PSO (Particle Swarm Optimization): One Method, Many Possible Applications
• VISPLORE: Exploring Particle Swarms by Visual Inspection.

Presenting state-of-the-art theory and practice in the field, this volume includes novel frameworks, a convergence and complexity analysis, and real-world applications. It aims to raise awareness of this effective method of enhancing evolutionary algorithms.

• A New Local Search Algorithm for the DNA Fragment Assembly Problem.- A Hybrid Immune-Based System for the Protein Folding Problem.- A Genetic Algorithm for the Resource Renting Problem with Minimum and Maximum Time Lags.- A Probabilistic Beam Search Approach to the Shortest Common Supersequence Problem.- Genetic Algorithms for Word Problems in Partially Commutative Groups.- A GRASP and Branch-and-Bound Metaheuristic for the Job-Shop Scheduling.- Reducing the Size of Traveling Salesman Problem Instances by Fixing Edges.- Iterated k-Opt Local Search for the Maximum Clique Problem.- Accelerating Local Search in a Memetic Algorithm for the Capacitated Vehicle Routing Problem.- Evolutionary Algorithms for Real-World Instances of the Automatic Frequency Planning Problem in GSM Networks.- A New Metaheuristic for the Vehicle Routing Problem with Split Demands.- Generation of Tree Decompositions by Iterated Local Search.- Edge Assembly Crossover for the Capacitated Vehicle Routing Problem.- Tackling the Container Loading Problem: A Hybrid Approach Based on Integer Linear Programming and Genetic Algorithms.- A Population-Based Local Search for Solving a Bi-objective Vehicle Routing Problem.- Combining Lagrangian Decomposition with an Evolutionary Algorithm for the Knapsack Constrained Maximum Spanning Tree Problem.- Exact/Heuristic Hybrids Using rVNS and Hyperheuristics for Workforce Scheduling.- An Analysis of Problem Difficulty for a Class of Optimisation Heuristics.- A New Grouping Genetic Algorithm for the Quadratic Multiple Knapsack Problem.- A Hybrid Method for Solving Large-Scale Supply Chain Problems.- Crossover Operators for the Car Sequencing Problem.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 7th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2007, held in Valencia, Spain in April 2007. The 21 revised full papers cover evolutionary algorithms as well as various other metaheuristics, like scatter search, tabu search, memetic algorithms, variable neighborhood search, ant colony optimization, and particle swarm optimization algorithms.
(source: Nielsen Book Data)

• Part 1 Introduction
• Part 2 Reinforcement Learning
• Part 3 On-Line Evolutionary Computation
• Part 4 Evolutionary Function Approximation
• Part 5 Sample-Efficient Evolutionary Function Approximation
• Part 6 Automatic Feature Selection for Reinforcement Learning
• Part 7 Adaptive Tile Coding
• Part 8 RelatedWork
• Part 9 Conclusion
• Part 10 Statistical Significance.

This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own representations have the potential to dramatically improve performance. This book introduces two novel approaches for automatically discovering high-performing representations. The first approach synthesizes temporal difference methods, the traditional approach to reinforcement learning, with evolutionary methods, which can learn representations for a broad class of optimization problems. This synthesis is accomplished by customizing evolutionary methods to the on-line nature of reinforcement learning and using them to evolve representations for value function approximators. The second approach automatically learns representations based on piecewise-constant approximations of value functions. It begins with coarse representations and gradually refines them during learning, analyzing the current policy and value function to deduce the best refinements. This book also introduces a novel method for devising input representations. This method addresses the feature selection problem by extending an algorithm that evolves the topology and weights of neural networks such that it evolves their inputs too. In addition to introducing these new methods, this book presents extensive empirical results in multiple domains demonstrating that these techniques can substantially improve performance over methods with manual representations.

• Section I Introduction.- Why Is Optimization Difficult?.- The Rationale behind Seeking Inspiration from Nature.-
• Section II Evolutionary Intelligence.- The Evolutionary-Gradient-Search Procedure in Theory and Practice.- The Evolutionary Transition Algorithm: Evolving Complex Solutions out of Simpler Ones.- A Model-Assisted Memetic Algorithm for Expensive Optimization Problems.- A Self-Adaptive Mixed Distribution Based Uni-variate Estimation of Distribution Algorithm for Large Scale Global Optimization.- Differential Evolution with Fitness Diversity Self-Adaptation.- Central Pattern Generators: Optimisation and Application.-
• Section III Collective Intelligence.- Fish School Search.- Magnifier Particle Swarm Optimization.- Improved Particle Swarm Optimization in Constrained Numerical Search Spaces.- Applying River Formation Dynamics to Solve NP-Complete Problems.-
• Section IV Social-Natural Intelligence.- Algorithms Inspired in Social Phenomena.- Artificial Immune Systems for Optimization.-
• Section V Multi-Objective Optimisation.- Ranking Methods in Many-objective Evolutionary Algorithms.- On the Effect of Applying a Steady-State Selection Scheme in the Multi-Objective Genetic Algorithm NSGA-II.- Improving the Performance of Multiobjective Evolutionary Optimization Algorithms using Coevolutionary Learning.- Evolutionary Optimization for Multiobjective Portfolio Selection Under Markowitz's Model with Application to the Caracas Stock Exchange.
• (source: Nielsen Book Data)

Nature-Inspired Algorithms are gaining much popularity in recent years due to the fact that many real-world optimisation problems have become increasingly large, complex and dynamic. The size and complexity of the problems nowadays require the development of methods and solutions whose efficiency is measured by their ability to find acceptable results within a reasonable amount of time, rather than an ability to guarantee the optimal solution. This volume "Nature-Inspired Algorithms for Optimisation" is a collection of the latest state-of-the-art algorithms and important studies for tackling various kinds of optimisation problems. It comprises 18 chapters, including two introductory chapters which address the fundamental issues that have made optimisation problems difficult to solve and explain the rationale for seeking inspiration from nature. The contributions stand out through their novelty and clarity of the algorithmic descriptions and analyses, and lead the way to interesting and varied new applications.
(source: Nielsen Book Data)

• Agents and Complex Systems.- Data Mining and Its Applications.- Evolutionary Algorithms and Optimization.- Intelligent Systems and their Applications.- Nature Inspired Creative Computing.- Smart Workspace and Image Processing.
• (source: Nielsen Book Data)

This PALO volume constitutes the Proceedings of the 19th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2015), held in Bangkok, Thailand, November 22-25, 2015. The IES series of conference is an annual event that was initiated back in 1997 in Canberra, Australia. IES aims to bring together researchers from countries of the Asian Pacific Rim, in the fields of intelligent systems and evolutionary computation, to exchange ideas, present recent results and discuss possible collaborations. Researchers beyond Asian Pacific Rim countries are also welcome and encouraged to participate. The theme for IES 2015 is "Transforming Big Data into Knowledge and Technological Breakthroughs". The host organization for IES 2015 is the School of Information Technology (SIT), King Mongkut's University of Technology Thonburi (KMUTT), and it is technically sponsored by the International Neural Network Society (INNS). IES 2015 is collocated with three other conferences; namely, The 6th International Conference on Computational Systems-Biology and Bioinformatics 2015 (CSBio 2015), The 7th International Conference on Advances in Information Technology 2015 (IAIT 2015) and The 10th International Conference on e-Business 2015 (iNCEB 2015), as a major part of series of events to celebrate the SIT 20th anniversary and the KMUTT 55th anniversary.
(source: Nielsen Book Data)

• EvoSET: Hybrid Algorithms Based on Integer Programming for the Search of Prioritized Test Data in Software Product Lines.- On the Use of Smelly Examples to Detect Code Smells in JavaScript.- Deep Parameter Tuning of Concurrent Divide and Conquer Algorithms in Akka.- Focusing Learning-based Testing away from Known Weaknesses.- Polytypic Genetic Programming.- Evolving Rules for Action Selection in Automated Testing via Genetic Programming - A First Approach.- EvoSTOC: A New Multi-swarm Particle Swarm Optimization for Robust Optimization over Time.- The Static and Stochastic VRP with Time Windows and Both Random Customers and Reveal Times.- Pre-Scheduled Colony Size Variation in Dynamic Environments.- An Online Packing Heuristic for the Three-dimensional Container Loading Problem in Dynamic Environments and the Physical Internet.- Advancing Dynamic Evolutionary Optimization Using In-Memory Database Technology.- Road Traffic Rules Synthesis Using Grammatical Evolution.- Solving Dynamic Graph Coloring Problem Using Dynamic Pool Based Evolutionary Algorithm.- General: Meta-Heuristics for Improved RF Emitter Localization.- Automated Design of Genetic Programming Classification Algorithms Using a Genetic Algorithm.
• (source: Nielsen Book Data)

The two volumes LNCS 10199 and 10200 constitute the refereed conference proceedings of the 20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017, held in Amsterdam, The Netherlands, in April 2017, colocated with the Evo* 2016 events EuroGP, EvoCOP, and EvoMUSART. The 46 revised full papers presented together with 26 poster papers were carefully reviewed and selected from 108 submissions. EvoApplications 2016 consisted of the following 13 tracks: EvoBAFIN (natural computing methods in business analytics and finance), EvoBIO (evolutionary computation, machine learning and data mining in computational biology), EvoCOMNET (nature-inspired techniques for telecommunication networks and other parallel and distributed systems), EvoCOMPLEX (evolutionary algorithms and complex systems), EvoENERGY (evolutionary computation in energy applications), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in image analysis, signal processing, and pattern recognition), EvoINDUSTRY (nature-inspired techniques in industrial settings), EvoKNOW (knowledge incorporation in evolutionary computation), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoROBOT (evolutionary robotics), EvoSET (nature-inspired algorithms in software engineering and testing), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments). .
(source: Nielsen Book Data)

• Diffusion of Shapes (R S Shaw & N H Packard)
• FDC-Based Particle Swarm Optimization (A Azzini et al.)
• Predictive Neural Networks in the Design of Experiments (D De March et al.)
• Prefrontal Cortex and Action Sequences: A Review on Neural Computational Models (I Gaudiello et al.)
• Bio-Inspired ICT for Evolutionary Emotional Intelligence (M Villamira & P Cipresso)
• Cooperation in Corvids: A Simulative Study with Evolved Robots (O Miglino et al.)
• Distributed Processes in a Agent-Based Model of Innovation (L Ansaloni et al.)
• Imaginary or Actual Artificial Worlds Using a New Tool in the ABM Perspective (P Terna)
• Dynamics of Interconnected Boolean Networks with Scale-Free Topology (C Damiani et al.)
• Semi-Synthetical Minimal Cells (P Stano & P L Luisi)
• and Other Papers.
• (source: Nielsen Book Data)

The Italian community in Artificial Life and Evolutionary computation has grown remarkably in recent years, and this book is the first broad collection of its major interests and achievements (including contributions from foreign countries). The contributions in Artificial Life as well as in Evolutionary Computation allow one to see the deep connections between the two fields. The topics addressed are extremely relevant for present day research in Artificial Life and in Evolutionary Computation, which include important contributions from very well-known researchers. The volume provides a very broad picture of the Italian activities in this field.
(source: Nielsen Book Data)

• Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews.- Quantum-Inspired Evolutionary Algorithm for Numerical Optimization.- Enhanced Evolutionary Algorithms for Multidisciplinary Design Optimization: A Control Engineering Perspective.- Hybrid Evolutionary Algorithms and Clustering Search.- A Novel Hybrid Algorithm for Function Optimization: Particle Swarm Assisted Incremental Evolution Strategy.- An Efficient Nearest Neighbor Classifier.- Hybrid Genetic: Particle Swarm Optimization Algorithm.- A Hybrid Genetic Algorithm and Bacterial Foraging Approach for Global Optimization and Robust Tuning of PID Controller with Disturbance Rejection.- Memetic Algorithms Parametric Optimization for Microlithography.- Significance of Hybrid Evolutionary Computation for Ab Initio Protein Folding Prediction.- A Hybrid Evolutionary Heuristic for Job Scheduling on Computational Grids.- Clustering Gene-Expression Data: A Hybrid Approach that Iterates Between k-Means and Evolutionary Search.- Robust Parametric Image Registration.- Pareto Evolutionary Algorithm Hybridized with Local Search for Biobjective TSP.
• (source: Nielsen Book Data)

This edited volume is targeted at presenting the latest state-of-the-art methodologies in "Hybrid Evolutionary Algorithms". The chapters deal with the theoretical and methodological aspects, as well as various applications to many real world problems from science, technology, business or commerce. Overall, the book has 14 chapters including an introductory chapter giving the fundamental definitions and some important research challenges. The contributions were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed.
(source: Nielsen Book Data)

• Theory.- Extremal Optimization Dynamics in Neutral Landscapes: The Royal Road Case.- Improving the Scalability of EA Techniques: A Case Study in Clustering.- Ant Colony Optimization.- MC-ANT: A Multi-Colony Ant Algorithm.- Applications.- Artificial Evolution for 3D PET Reconstruction.- A Hybrid Genetic Algorithm/Variable Neighborhood Search Approach to Maximizing Residual Bandwidth of Links for Route Planning.- Parallelization of an Evolutionary Algorithm on a Platform with Multi-core Processors.- On the Difficulty of Inferring Gene Regulatory Networks: A Study of the Fitness Landscape Generated by Relative Squared Error.- Combinatorial Optimization.- Memetic Algorithms for Constructing Binary Covering Arrays of Strength Three.- A Priori Knowledge Integration in Evolutionary Optimization.- Robotics.- On-Line, On-Board Evolution of Robot Controllers.- The Transfer of Evolved Artificial Immune System Behaviours between Small and Large Scale Robotic Platforms.- Multi-objective Optimization.- An Analysis of Algorithmic Components for Multiobjective Ant Colony Optimization: A Case Study on the Biobjective TSP.- Alternative Fitness Assignment Methods for Many-Objective Optimization Problems.- Genetic Programming.- Evolving Efficient List Search Algorithms.- Semantic Similarity Based Crossover in GP: The Case for Real-Valued Function Regression.- Genetic-Programming Based Prediction of Data Compression Saving.- Machine Learning.- On the Characteristics of Sequential Decision Problems and Their Impact on Evolutionary Computation and Reinforcement Learning.
• (source: Nielsen Book Data)

This LNCS volume contains the best papers presented at the 9th Conference on 1 Arti?cial Evolution, EA 2009, held in Strasbourg (France). Previous EA events took place in Tours (2007, LNCS 4926), Lille (2005, LNCS 3871), Marseille (2003, LNCS 2936), Le Creusot (2001, LNCS 2310), Dunkerque (1999, LNCS 1829), Nimes (1997, LNCS 1363), Brest (1995, LNCS 1063) and Toulouse (1994, LNCS 1063). For this ninth edition, authors were invited to present their originalwork r- evantto arti?cialevolution, including, butnotlimitedto:evolutionarycompu- tion, evolutionaryoptimization, co-evolution, arti?ciallife, populationdynamics, theory, algorithmicsand modeling, implementations, application of evolutionary paradigmstotherealworld(industry, biosciences, ...), otherbiologically-inspired paradigms (swarm, arti?cial ants, arti?cial immune systems, ...), memetic al- rithms, multi-objective optimization, constraint handling, parallel algorithms, dynamic optimization, machine learning and hybridization with other soft c- puting techniques. Submitted papers were reviewed by at least four members of the Inter- tional ProgramCommittee, which selected 23 papers to be presented during the conference out of 43 submissions. However, only 17 papers were included in the present volume, resulting in a 39.5% acceptance rate. We would like to thank the members of the Program Committee for their conscientious work, the authors for their greatly appreciated contributions, but also the members of the Organizing Committee who, once more, managed to put together a really enjoyable conference in Strasbourg. Finally, ?nancial and material support coming from ENSPS, Universit'ede Strasbourg, CNRS, R' egion Alsace and the EA association contributed to the success of the conference and helped to keep registration fees very low.
(source: Nielsen Book Data)

• EvoBAFIN: Enhanced Multiobjective Population-Based Incremental Learning with Applications in Risk Treaty Optimization
• Genetic Programming with Memory for Financial Trading
• Improving Fitness Functions in Genetic Programming for Classification on Unbalanced Credit Card Data
• Evolving Classification Models for Prediction of Patient Recruitment in Multicentre Clinical Trials Using Grammatical Evolution
• Portfolio Optimization, a Decision-Support Methodology for Small Budgets
• Evolutionary Multiobjective Optimization for Portfolios in Emerging Markets: Contrasting Higher Moments and Median Models
• EvoBIO: On Combinatorial Optimisation in Analysis of Protein-Protein Interaction and Protein Folding Networks
• A Multi-objective Genetic Programming Biomarker Detection Approach in Mass Spectrometry Data
• Automating Biomedical Data Science Through Tree-Based Pipeline Optimization
• Bicliques in Graphs with Correlated Edges: From Artificial to Biological Networks
• Hybrid Biclustering Algorithms for Data Mining
• Discovering Potential Clinical Profiles of Multiple Sclerosis from Clinical and Pathological Free Text Data with Constraint Non-negative Matrix Factorization
• Application of Evolutionary Algorithms for the Optimization of Genetic Regulatory Networks
• EvoCOMNET: A Hybrid Discrete Artificial Bee Colony Algorithm for the Multicast Routing Problem
• Evolving Coverage Optimisation Functions for Heterogeneous Networks Using Grammatical Genetic Programming
• Joint Topology Optimization, Power Control and Spectrum Allocation for Intra-Vehicular Multi-hop Sensor Networks Using Dandelion-Encoded Heuristics
• A Heuristic Crossover Enhanced Evolutionary Algorithm for Clustering Wireless Sensor Network
• A Variable Local Search Based Memetic Algorithm for the Load Balancing Problem in Cloud Computing
• An (MI)LP-Based Primal Heuristic for 3-Architecture Connected Facility Location in Urban Access Network Design
• Reducing Efficiency of Connectivity-Splitting Attack on Newscast via Limited Gossip
• A Distributed Intrusion Detection Framework Based on Evolved Specialized Ensembles of Classifiers
• UAV Fleet Mobility Model with Multiple Pheromones for Tracking Moving Observation Targets
• EvoCOMPLEX: Towards Intelligent Biological Control: Controlling Boolean Networks with Boolean Networks
• The Emergence of Cooperation in Public Goods Games on Randomly Growing Dynamic Networks
• Influence Maximization in Social Networks with Genetic Algorithms
• Measuring Diversity of Socio-Cognitively Inspired ACO Search
• Multiwinner Voting in Genetic Algorithms for Solving Ill-Posed Global Optimization Problems
• EvoENERGY: A Decentralized PSO with Decoder for Scheduling Distributed Electricity Generation
• Comparison of Multi-objective Evolutionary Optimization in Smart Building Scenarios
• Stigmergy-Based Scheduling of Flexible Loads
• Electrical Load Pattern Shape Clustering Using Ant Colony Optimization
• Optimization of Operation and Control Strategies for Battery Energy Storage Systems by Evolutionary Algorithms
• EvoGAMES: Orthogonally Evolved AI to Improve Difficulty Adjustment in Video Games
• There Can Be only One: Evolving RTS Bots via Joust Selection
• Constrained Level Generation through Grammar-Based Evolutionary Algorithms
• Evolving Chess-Like Games Using Relative Algorithm Performance Profiles
• Online Evolution for Multi-action Adversarial Games
• The Story of Their Lives: Massive Procedural Generation of Heroes' Journeys Using Evolved Agent-Based Models and Logical Reasoning
• Dangerousness Metric for Gene Regulated Car Driving
• Using Isovists to Evolve Terrains with Gameplay Elements
• A Spatially-Structured PCG Method for Content Diversity in a Physics-Based simulation Game
• Design and Evaluation of an Extended Learning Classifier-Based StarCraft Micro AI
• EvoIASP: A Wrapper Feature Selection Approach to Classification with Missing Data
• Bare-Bone Particle Swarm Optimisation for Simultaneously Discretising and Selecting Features For High-Dimensional Classification
• Mutual Information Estimation for Filter Based Feature Selection Using Particle Swarm Optimization
• Speaker Verification on Unbalanced Data with Genetic Programming
• Binary Tomography Reconstruction by Particle Aggregation
• Population Based Ant Colony Optimization for Reconstructing ECG Signals
• EvoINDUSTRY: Can Evolutionary Algorithms Beat Dynamic Programming for Hybrid
• NSGA-II Based Auto-Calibration of Automatic Number Plate Recognition Camera for Vehicle Speed Measurement
• Environment-Model Based Testing with Differential Evolution in an Industrial Setting
• Workforce Scheduling in Inbound Customer Call Centres with a Case Study.

The two volumes LNCS 9597 and 9598 constitute the refereed conference proceedings of the 19th European Conference on the Applications of Evolutionary Computation, EvoApplications 2016, held in Porto, Portugal, in March/April 2016, co-located with the Evo* 2016 events EuroGP, EvoCOP, and EvoMUSART. The 57 revised full papers presented together with 17 poster papers were carefully reviewed and selected from 115 submissions. EvoApplications 2016 consisted of the following 13 tracks: EvoBAFIN (natural computing methods in business analytics and finance), EvoBIO (evolutionary computation, machine learning and data mining in computational biology), EvoCOMNET (nature-inspired techniques for telecommunication networks and other parallel and distributed systems), EvoCOMPLEX (evolutionary algorithms and complex systems), EvoENERGY (evolutionary computation in energy applications), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in image analysis, signal processing, and pattern recognition), EvoINDUSTRY (nature-inspired techniques in industrial settings), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for risk management, security and defence applications), EvoROBOT (evolutionary robotics), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).

• EvoNUM: Local Fitness Meta-Models with Nearest Neighbor Regression
• Validating the Grid Diversity Operator: an Infusion Technique for Diversity Maintenance in Population-Based Optimisation Algorithms
• Benchmarking Languages for Evolutionary Algorithms
• On the Closest Averaged Hausdorff Archive for a Circularly Convex Pareto Front
• Evolving Smoothing Kernels for Global Optimization
• EvoPAR: Implementing Parallel Differential Evolution on Spark
• ECJ+HADOOP: An Easy Way to Deploy Massive Runs of Evolutionary Algorithm
• Addressing High Dimensional Multi-Objective Optimization Problems by Coevolutionary Islands with Overlapping Search Spaces
• Compilable Phenotypes: Accelerating the Evaluation of Individuals in Grammatical Evolution
• GPU Accelerated Molecular Docking Simulation with Genetic Algorithms
• EvoRISK: Challenging Anti-virus Through Evolutionary Malware Obfuscation
• EvoROBOT: Leveraging Online Racing and Population Cloning in Evolutionary Multirobot Systems
• Multi-Agent Behavior-Based Policy Transfer
• On-line Evolution of Foraging Behaviour in a Population of Real Robots
• Hybrid Control for a Real Swarm Robotics System in an Intruder Detection Task
• EvoSTOC: Direct Memory Schemes for Population-Based Incremental Learning in Cyclically Changing Environments
• Simheuristics for the Multiobjective Nondeterministic Firefighter Problem in a Time-Constrained Setting
• Benchmarking Dynamic Three-Dimensional Bin Packing Problems Using Discrete-Event Simulation
• Genetic Programming Algorithms for Dynamic Environments
• A Memory-Based NSGA-II Algorithm for Dynamic Multi-Objective Optimization Problems
• Hybrid Dynamic Resampling Algorithms for Evolutionary Multi-objective Optimization of Invariant-Noise Problems.

The two volumes LNCS 9597 and 9598 constitute the refereed conference proceedings of the 19th European Conference on the Applications of Evolutionary Computation, EvoApplications 2016, held in Porto, Portugal, in March/April 2016, co-located with the Evo* 2016 events EuroGP, EvoCOP, and EvoMUSART. The 57 revised full papers presented together with 17 poster papers were carefully reviewed and selected from 115 submissions. EvoApplications 2016 consisted of the following 13 tracks: EvoBAFIN (natural computing methods in business analytics and finance), EvoBIO (evolutionary computation, machine learning and data mining in computational biology), EvoCOMNET (nature-inspired techniques for telecommunication networks and other parallel and distributed systems), EvoCOMPLEX (evolutionary algorithms and complex systems), EvoENERGY (evolutionary computation in energy applications), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in image analysis, signal processing, and pattern recognition), EvoINDUSTRY (nature-inspired techniques in industrial settings), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for risk management, security and defence applications), EvoROBOT (evolutionary robotics), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).

• Preface Acknowledgments Introduction Contrasting Representations for Maze Generation Dual Mazes Terrain Maps Cellular Automata Based Maps Decomposition, Tiling, and Assembly Bibliography Author's Biography.
• (source: Nielsen Book Data)

Automatic content generation is the production of content for games, web pages, or other purposes by procedural means. Search-based automatic content generation employs search-based algorithms to accomplish automatic content generation. This book presents a number of different techniques for search-based automatic content generation where the search algorithm is an evolutionary algorithm. The chapters treat puzzle design, the creation of small maps or mazes, the use of L-systems and a generalization of L-system to create terrain maps, the use of cellular automata to create maps, and, finally, the decomposition of the design problem for large, complex maps culminating in the creation of a map for a fantasy game module with designersupplied content and tactical features. The evolutionary algorithms used for the different types of content are generic and similar, with the exception of the novel sparse initialization technique are presented in Chapter 2. The points where the content generation systems vary are in the design of their fitness functions and in the way the space of objects being searched is represented. A large variety of different fitness functions are designed and explained, and similarly radically different representations are applied to the design of digital objects all of which are, essentially, maps for use in games.
(source: Nielsen Book Data)

• Section I: Analysis of Genetic Algorithms
• A Novel Online Test-Sheet Composition Approach Using Genetic Algorithm
• A Route System Based on Genetic Algorithm for Coarse-Grain Reconfigurable Architecture
• Building Trade System by Genetic Algorithm
• Implementation of Parallel Genetic Algorithm Based on CUDA
• Moitf GibbsGA: Sampling Transcription Factor Binding Sites Coupled with PSFM Optimization by GA
• Network Model and Optimization of Medical Waste Reverse Logistics by Improved Genetic Algorithm
• SGEGC: A Selfish Gene Theory Based Optimization Method by Exchanging Genetic Components
• Section II: Computational Intelligence in Engineer Design
• A Novel RM-Based Algorithm for Reversible Circuits
• A Novel Transformation-Based Algorithm for Reversible Logic Synthesis
• Estimation of Distribution Algorithms for the Machine-Part Cell Formation
• Global Exponential Stability of Delayed Neural Networks with Non-lipschitz Neuron Activations and Impulses
• Modeling and Verification of Zhang Neural Networks for Online Solution of Time-Varying Quadratic Minimization and Programming
• New Product Design Based Target Cost Control with BP Neural Network and Genetic Algorithm
• A Case Study in Chinese Automobile Industry
• Hard Real Time Task Oriented Power Saving Scheduling Algorithm Based on DVS
• Section III: Optimization and Learning
• A Globally Convergent Smoothing Method for Symmetric Conic Linear Programming
• A New Optimizaiton Algorithm for Function Optimization
• A Point Symmetry-Based Automatic Clustering Approach Using Differential Evolution
• Balanced Learning for Ensembles with Small Neural Networks
• Estimating Geostatistics Variogram Parameters Based on Hybrid Orthogonal Differential Evolution Algorithm
• Memetic Strategies for Global Trajectory Optimisation
• Effects of Similarity-Based Selection on WBMOIA: A Weight-Based Multiobjective Immune Algorithm
• Section IV: Representations and Operators
• A Novel Evolutionary Algorithm Based on Multi-parent Crossover and Space Transformation Search
• An Evolutionary Algorithm and Kalman Filter Hybrid Approach for Integrated Navigation
• Clonal and Cauchy-mutation Evolutionary Algorithm for Global Numerical Optimization
• Construction of Hoare Triples under Generalized Model with Semantically Valid Genetic Operations
• Evaluation of Cobalt-Rich Crust Resources Based on Fractal Characteristics of Seamount Terrain
• Hybridizing Evolutionary Negative Selection Algorithm and Local Search for Large-Scale Satisfiability Problems
• Novel Associative Memory Retrieving Strategies for Evolutionary Algorithms in Dynamic Environments
• Section V: Robust Classification
• An Algorithm of Mining Class Association Rules
• An Empirical Study on Several Classification Algorithms and Their Improvements
• Classification of Imbalanced Data Sets by Using the Hybrid Re-sampling Algorithm Based on Isomap
• Detecting Network Anomalies Using CUSUM and EM Clustering
• Multiobjective Optimization in Mineral Resources Exploitation: Models and Case Studies
• Robust and Efficient Eye Location and Its State Detection
• Section VI: Statistical Learning
• A Neural Network Architecture for Perceptual Grouping, Attention Modulation and Boundary-Surface Interaction
• Adaptive Neighborhood Select Based on Local Linearity for Nonlinear Dimensionality Reduction
• Anti-spam Filters Based on Support Vector Machines
• Multi-attribute Weight Allocation Based on Fuzzy Clustering Analysis and Rough Sets
• Spatio-temporal Model Based on Back Propagation Neural Network for Regional Data in GIS
• Subject Integration and Applications of Neural Networks
• The Convergence Control to the ACO Metaheuristic Using Annotated Paraconsistent Logic
• The Research of Artificial Neural Network on Negative Correlation Learning
• Section VII: Swarm Intelligence
• A Discrete PSO for Multi-objective Optimization in VLSI Floorplanning
• Applying Chaotic Particle Swarm Optimization to the Template Matching Problem
• Cellular PSO: A PSO for Dynamic Environments
• Constrained Layout Optimization Based on Adaptive Particle Swarm Optimizer
• Multi-swarm Particle Swarm Optimizer with Cauchy Mutation for Dynamic Optimization Problems
• Optimization of the Damping of the Rectangular 3-D Braided Composite Based on PSO Algorithm
• Parallel Hybrid Particle Swarm Optimization and Applications in Geotechnical Engineering
• Storage-Based Intrusion Detection Using Artificial Immune Technique
• Section VIII: System Design
• A New Method for Optimal Configuration of Weapon System
• A Secure Routing Algorithm for MANET
• Detection and Defense of Identity Attacks in P2P Network
• License Plate Multi-DSP and Multi-FPGA Design and Realization in Highway Toll System
• QoS Routing Algorithm for Wireless Multimedia Sensor Networks
• Realization of Fingerprint Identification on DSP
• Localization Algorithm of Beacon-Free Node in WSN Based on Probability
• The Method of Knowledge Processing in Intelligent Design System of Products.

Annotation---Volumes CCIS 51 and LNCS 5812 constitute the proceedings of the Fourth Interational Symposium on Intelligence Computation and Applications, ISICA 2009, held in Huangshi, China, during October 23-25. ISICA 2009 attracted over 300 submissions. Through rigorous reviews, 58 papers were included in LNCS 5821, and 54 papers were collected in CCIS 51. ISICA conferences are one of the first series of international conferences on computational intelligence that combine elements of learning, adaptation, evolution and fuzzy logic to create programs as alternative solutions to artificial intelligence.

• Part 1 - Setting the Stage
• Chapter 1 - Intelligent Machines: Imitating Life
• Chapter 2 - Deep Blue: A Triumph of AI?
• Chapter 3 - Building An Artificial Brain
• Chapter 4 - Evolutionary Computation: Putting Nature to Work
• Chapter 5 - Blue Hawaii: Natural Selection
• Chapter 6 - Checkers
• Chapter 7 - Chinook: The Man-machine Checkers Champion
• Chapter 8 - Samuel's Learning Machine
• Chapter 9 - The Samuel-Newell Challenge
• Part 2 - The Making of Blondie
• Chapter 10 - Evolving in the Checkers Environment
• Chapter 11 - In The Zone
• Chapter 12 - A Repeat Performance
• Chapter 13 - A New Dimension
• Chapter 14 - Letting the Genie Out of the Bottle
• Chapter 15 - Blondie24 Epilogue: The Future of Artificial Intelligence Appendix: Your Honor, I Object! Notes Index About the Author.
• (source: Nielsen Book Data)

Blondie24 tells the story of a computer that taught itself to play checkers far better than its creators ever could by using a program that emulated the basic principles of Darwinian evolution--random variation and natural selection-- to discover on its own how to excel at the game. Unlike Deep Blue, the celebrated chess machine that beat Garry Kasparov, the former world champion chess player, this evolutionary program didn't have access to strategies employed by human grand masters, or to databases of moves for the endgame moves, or to other human expertise about the game of chekers. With only the most rudimentary information programmed into its "brain, " Blondie24 (the program's Internet username) created its own means of evaluating the complex, changing patterns of pieces that make up a checkers game by evolving artificial neural networks---mathematical models that loosely describe how a brain works. It's fitting that Blondie24 should appear in 2001, the year when we remember Arthur C. Clarke's prediction that one day we would succeed in creating a thinking machine. In this compelling narrative, David Fogel, author and co-creator of Blondie24, describes in convincing detail how evolutionary computation may help to bring us closer to Clarke's vision of HAL. Along the way, he gives readers an inside look into the fascinating history of AI and poses provocative questions about its future.
(source: Nielsen Book Data)

• From Feature Selection to Continuous Optimization.- Evolving a Weighted Combination of Text Similarities for Authorship Attribution.- Image Signal Processor Parameter Tuning with Surrogate-assisted Particle Swarm Optimization.- Combinatorial Surrogate-assisted Optimization for Bus Stops Spacing Problem.- Optimization of a Checkers Player Using Neural and Metaheuristic Approaches.- A Novel Outlook on Feature Selection as a Multi-Objective Problem.- Fast Evolutionary Algorithm for Solving Large-scale Multi-objective Problems.- Looking for Energy Efficient Genetic Algorithms.- Evolving Fitness Landscapes with Complementary Fitness Functions.- Bayesian Immigrant Diploid Genetic Algorithm for Dynamic Environments.- Ant Colony Optimization Algorithm for a Transportation Problem in Home Health Care with the Consideration of Carbon Emissions.- Selective Vehicle Routing Problem: A Hybrid Genetic Algorithm Approach.- Fixed Jobs Multi-agent Scheduling Problem with Renewable Resources.- A Study of Recombination Operators for the Cyclic Bandwidth Problem.- Automatic Calibration of a Farm Irrigation Model: a Multi-modal Optimization Approach.- A Hybrid Evolutionary Algorithm for Offl ine UAV Path Planning.
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed post-conference proceedings of the 14th International Conference on Artificial Evolution, EA 2019, held in Mulhouse, France, in October 2019. The 16 revised papers were carefully reviewed and selected from 33 submissions. The papers cover a wide range of topics in the field of artificial evolution, such as evolutionary computation, evolutionary optimization, co-evolution, artificial life, population dynamics, theory, algorithmic and modeling, implementations, application of evolutionary paradigms to the real world (industry, biosciences...), other biologically-inspired paradigms (swarm, artificial ants, artificial immune systems, cultural algorithms...), memetic algorithms, multi-objective optimization, constraint handling, parallel algorithms, dynamic optimization, machine learning and hybridization with other soft computing techniques.
(source: Nielsen Book Data)

• Towards an Assistive Social Robot Interacting with Human Patient to Establish a Mutual and Effective Support.- Selecting for Positive Responses to Knock outs in Boolean Networks.- Avalanches of Perturbations in Modular Gene Regulatory Networks.- The Effects of a Simplified Model of Chromatin Dynamics on Attractors Robustness in Random Boolean Networks with Self-loops: an Experimental Study.- A Memetic Approach for the Orienteering Problem.- The Detection of Dynamical Organization in Cancer Evolution Models.- The Simulation of Noise Impact on the Dynamics of a Discrete Chaotic Map.- Exploiting Distributed Discrete-Event Simulation Techniques for Parallel Execution of Cellular Automata.- A Relevance Index-based Method for Improved Detection of Malicious Users in Social Networks.- An Analysis of Cooperative Coevolutionary Differential Evolution as Neural Networks Optimizer.- Design and Evaluation of a Heuristic Optimization Tool Based on Evolutionary Grammars Using PSoCs.- How Word Choice Affects Cognitive Impairment Detection by Handwriting Analysis: a Preliminary Study.- Modeling the Coordination of a Multiple Robots Using Nature Inspired Approaches.- Nestedness Temperature in the Agent-Artifact Space: Emergence of Hierarchical Order in the 2000-2014 Photonics Techno-Economic Complex System.- Towards Programmable Chemistries.- Studying and Simulating the Three-dimensional Arrangement of Droplets.- Investigating Three-dimensional Arrangements of Droplets.
• (source: Nielsen Book Data)

This book constitutes the revised selected papers of the 14th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2019, held in Rende, Italy, in September 2019. The 13 full papers and 4 short paper presented were thoroughly reviewed and selected from 31 submissions. They are focused on the topics of information systems, design and analysis of algorithms, artificial intelligence, machine learning, cognitive science, modeling and simulation, collaborative and social computing, parallel computing, distributed computing. The chapter "Nestedness Temperature in the Agent-Artifact Space: Emergence of Hierarchical Order in the 2000-2014 Photonics Techno-Economic Complex System" is available open access under a CC BY 4.0 license.
(source: Nielsen Book Data)

• Introduction to Evolutionary Data Clustering and its Applications.- A Comprehensive Review of Evaluation and Fitness Measures for Evolutionary Data Clustering.- A Grey Wolf based Clustering Algorithm for Medical Diagnosis Problems.- EEG-based Person Identification Using Multi-Verse Optimizer As Unsupervised Clustering Techniques.- Review of Evolutionary Data Clustering Algorithms for Image Segmentation.- Classification Approach based on Evolutionary Clustering and its Application for Ransomware Detection.
• (source: Nielsen Book Data)

This book provides an in-depth analysis of the current evolutionary clustering techniques. It discusses the most highly regarded methods for data clustering. The book provides literature reviews about single objective and multi-objective evolutionary clustering algorithms. In addition, the book provides a comprehensive review of the fitness functions and evaluation measures that are used in most of evolutionary clustering algorithms. Furthermore, it provides a conceptual analysis including definition, validation and quality measures, applications, and implementations for data clustering using classical and modern nature-inspired techniques. It features a range of proven and recent nature-inspired algorithms used to data clustering, including particle swarm optimization, ant colony optimization, grey wolf optimizer, salp swarm algorithm, multi-verse optimizer, Harris hawks optimization, beta-hill climbing optimization. The book also covers applications of evolutionary data clustering in diverse fields such as image segmentation, medical applications, and pavement infrastructure asset management.
(source: Nielsen Book Data)

• Evolutionary Computation
• Evolutionary Optimization, Co-evolution,
• Artificial Life
• Population Dynamics
• Theory, Algorithmics and Modeling
• Implementations
• Application of Evolutionary Paradigms to the Real World.

This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Conference on Artificial Evolution, EA 2015, held in Lyon, France, in October 2015. The 18 revised papers were carefully reviewed and selected from 31 submissions. The focus of the conference is on following topics: Evolutionary Computation, Evolutionary Optimization, Co-evolution, Artificial Life, Population Dynamics, Theory, Algorithmics and Modeling, Implementations, Application of Evolutionary Paradigms to the Real World, Dynamic Optimization, Machine Learning and hybridization with other soft computing techniques.

• Nature Inspired Constrained Optimization.- Recent Advances on Evolutionary Optimization Technologies.- Wearable Computing and Intelligent Data Hiding.- Image Processing and Intelligent Applications.- Intelligent Multimedia Tools and Applications.- Technologies for Next-Generation Network Environments.
• (source: Nielsen Book Data)

This volume of Advances in Intelligent Systems and Computing contains accepted papers presented at ICGEC 2014, the 8th International Conference on Genetic and Evolutionary Computing. The conference this year was technically co-sponsored by Nanchang Institute of Technology in China, Kaohsiung University of Applied Science in Taiwan, and VSB-Technical University of Ostrava. ICGEC 2014 is held from 18-20 October 2014 in Nanchang, China. Nanchang is one of is the capital of Jiangxi Province in southeastern China, located in the north-central portion of the province. As it is bounded on the west by the Jiuling Mountains, and on the east by Poyang Lake, it is famous for its scenery, rich history and cultural sites. Because of its central location relative to the Yangtze and Pearl River Delta regions, it is a major railroad hub in Southern China. The conference is intended as an international forum for the researchers and professionals in all areas of genetic and evolutionary computing.
(source: Nielsen Book Data)

• A Computational Study of Neighborhood Operators for Job-shop Scheduling Problems with Regular Objectives.- A Genetic Algorithm for Multi-Component Optimization Problems: the Case of the Travelling Thief Problem.- A Hybrid Feature Selection Algorithm Based on Large Neighborhood Search.- A Memetic Algorithm to Maximise the Employee Substitutability in Personnel Shift Scheduling.- Construct, Merge, Solve and Adapt versus Large Neighborhood Search for Solving the Multi-Dimensional Knapsack Problem: Which One Works Better When.- Decomposing SAT Instances with Pseudo Backbones.- Efficient Consideration of Soft Time Windows in a Large Neighborhood Search for the Districting and Routing Problem for Security Control.- Estimation of Distribution Algorithms for the Firefighter Problem.- LCS-Based Selective Route Exchange Crossover for the Pickup and Delivery Problem with Time Windows.- Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO.- Optimizing Charging Station Locations for Electric Car-Sharing Systems.- Selection of Auxiliary Objectives Using Landscape Features and Offline Learned Classifier.- Sparse, Continuous Policy Representations for Uniform Online Bin Packing via Regression of Interpolants.- The Weighted Independent Domination Problem: ILP Model and Algorithmic .
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 17th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2017, held in Amsterdam, The Netherlands, in April 2017, co-located with the Evo*2017 events EuroGP, EvoMUSART and EvoApplications. The 16 revised full papers presented were carefully reviewed and selected from 39 submissions. The papers cover both empirical and theoretical studies on a wide range of academic and real-world applications. The methods include evolutionary and memetic algorithms, large neighborhood search, estimation of distribution algorithms, beam search, ant colony optimization, hyper-heuristics and matheuristics. Applications include both traditional domains, such as knapsack problem, vehicle routing, scheduling problems and SAT; and newer domains such as the traveling thief problem, location planning for car-sharing systems and spacecraft trajectory optimization. Papers also study important concepts such as pseudo-backbones, phase transitions in local optima networks, and the analysis of operators. This wide range of topics makes the EvoCOP proceedings an important source for current research trends in combinatorial optimization.
(source: Nielsen Book Data)

• A Hybrid Constructive Mat-Heuristic Algorithm for The Heterogeneous Vehicle Routing Problem with Simultaneous Pick-up and Delivery
• A Property Preserving Method for Extending a Single-Objective Problem Instance to Multiple Objectives with Specific Correlations
• An Evolutionary Approach to the Full Optimization of the Traveling Thief Problem
• Construct, Merge, Solve & Adapt: Application to the Repetition-Free Longest Common Subsequence Problem
• Deconstructing the Big Valley Search Space Hypothesis
• Determining the Difficulty of Landscapes by PageRank Centrality in Local Optima Networks
• Efficient Hill Climber for Multi-Objective Pseudo-Boolean Optimization
• Evaluating Hyperheuristics and Local Search Operators for Periodic Routing Problems
• Evolutionary Algorithms for Finding Short Addition Chains: Going the Distance
• Experimental Evaluation of Two Approaches to Optimal Recombination for Permutation Problems
• Hyperplane Elimination for Quickly Enumerating Local Optima
• Limits to Learning in Reinforcement Learning Hyperheuristics
• Modifying Colourings between Time-Steps to Tackle Changes in Dynamic Random Graphs
• Particle Swarm Optimisation with Sequence-Like Indirect Representation for Web Service Composition
• Particle Swarm Optimization for Multi-Objective Web Service Location Allocation
• Sim-EDA: A Multipopulation Estimation of Distribution Algorithm Based on Problem Similarity
• Solving the Quadratic Assignment Problem with Cooperative Parallel Extremal Optimization.

This book constitutes the refereed proceedings of the 16th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2016, held in Porto, Portugal, in March/April 2016, co-located with the Evo*2015 events EuroGP, EvoMUSART and EvoApplications. The 17 revised full papers presented were carefully reviewed and selected from 44 submissions. The papers cover methodology, applications and theoretical studies. The methods included evolutionary and memetic algorithms, variable neighborhood search, particle swarm optimization, hyperheuristics, mat-heuristic and other adaptive approaches. Applications included both traditional domains, such as graph coloring, vehicle routing, the longest common subsequence problem, the quadratic assignment problem; and new(er) domains such as the traveling thief problem, web service location, and finding short addition chains. The theoretical studies involved fitness landscape analysis, local search and recombination operator analysis, and the big valley search space hypothesis. The consideration of multiple objectives, dynamic and noisy environments was also present in a number of articles.

• Evolutionary computation
• Bioinspired algorithms
• Genetic algorithms, bioinformatics and computational biology
• Modeling and simulation of artificial and biological systems
• Complex systems, synthetic and systems biology
• Systems chemistry.

This book constitutes the revised selected papers of the 10th Italian Workshop on Advances in Artificial Life, Evolutionary Computation and Systems Chemistry, WIVACE 2015, held at Bari, Italy, in September 2015. The 18 papers presented have been thoroughly reviewed and selected from 45 submissions. They cover the following topics: evolutionary computation, bioinspired algorithms, genetic algorithms, bioinformatics and computational biology, modeling and simulation of artificial and biological systems, complex systems, synthetic and systems biology, systems chemistry.

• Part 1: Fascinating Evolution
• Part 2: Evolutionary Computation
• Part 3: If Darwin was a Programmer
• Part 4: Evolution of Developmental Programs.
• (source: Nielsen Book Data)

An essential capacity of intelligence is the ability to learn. An artificially intelligent system that could learn would not have to be programmed for every eventuality; it could adapt to its changing environment and conditions just as biological systems do. Illustrating Evolutionary Computation with Mathematica introduces evolutionary computation to the technically savvy reader who wishes to explore this fascinating and increasingly important field. Unique among books on evolutionary computation, the book also explores the application of evolution to developmental processes in nature, such as the growth processes in cells and plants. If you are a newcomer to the evolutionary computation field, an engineer, a programmer, or even a biologist wanting to learn how to model the evolution and coevolution of plants, this book will provide you with a visually rich and engaging account of this complex subject.
(source: Nielsen Book Data)

• AI Techniques and applications.- Data Analytics and Technologies.- Data Mining and Image Processing.- Machine & Statistical Learning. .
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 6th International Conference on Soft Computing in Data Science, SCDS 2021, which was held virtually in November 2021. The 31 revised full papers presented were carefully reviewed and selected from 79 submissions. The papers are organized in topical sections on AI techniques and applications; data analytics and technologies; data mining and image processing; machine & statistical learning. .
(source: Nielsen Book Data)

• Chapter 1: An Enhanced Model for Digital Reference Services (MDRS).-
• Chapter 2: Fuzzy Random Based Mean Variance Model For Agricultural Production Planning.-
• Chapter 3: Residual Neural Network vs Local Binary Convolutional Neural Networks for Bilingual Handwritten Digit Recognition.-
• Chapter 4: Incorporating the Markov Chain Model in WBSN for Improving Patients' Remote Monitoring Systems.-
• Chapter 5: Designing Deep Neural Network with Chicken Swarm Optimization for Violence Video Classification using VSD2014 Dataset.-
• Chapter 6: Header Based Email Spam Detection Framework Using Support Vector Machine (SVM) Technique.-
• Chapter 7: A Mechanism to Support Agile Frameworks Enhancing Reliability Assessment for SCS Development: A Case Study of Medical Surgery Departments.-
• Chapter 8: Link Bandwidth Recommendation for Indonesian E-Health Grid.-
• Chapter 9: Investigating the Optimal Parameterization of Deep Neural Network and Synthetic Data Workflow for Imbalance Liver Disorder Dataset Classification.-
• Chapter 10: Genetic Algorithm Based Parallel K-Means Data Clustering Algorithm Using MapReduce Programming Paradigm on Hadoop Environment (GAPKCA).-
• Chapter 11: Android Botnet Detection by Classification Techniques.
• (source: Nielsen Book Data)

This book provides an introduction to data science and offers a practical overview of the concepts and techniques that readers need to get the most out of their large-scale data mining projects and research studies. It discusses data-analytical thinking, which is essential to extract useful knowledge and obtain commercial value from the data. Also known as data-driven science, soft computing and data mining disciplines cover a broad interdisciplinary range of scientific methods and processes. The book provides readers with sufficient knowledge to tackle a wide range of issues in complex systems, bringing together the scopes that integrate soft computing and data mining in various combinations of applications and practices, since to thrive in these data-driven ecosystems, researchers, data analysts and practitioners must understand the design choice and options of these approaches. This book helps readers to solve complex benchmark problems and to better appreciate the concepts, tools and techniques used.
(source: Nielsen Book Data)

• Automated Algorithm Selection and Configuration
• Bayesian- and Surrogate-Assisted Optimization
• Benchmarking and Performance Measures
• Combinatorial Optimization
• Connection Between Nature-Inspired Optimization and Artificial Intelligence
• Genetic and Evolutionary Algorithms.

This two-volume set LNCS 12269 and LNCS 12270 constitutes the refereed proceedings of the 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020, held in Leiden, The Netherlands, in September 2020. The 99 revised full papers were carefully reviewed and selected from 268 submissions. The topics cover classical subjects such as automated algorithm selection and configuration; Bayesian- and surrogate-assisted optimization; benchmarking and performance measures; combinatorial optimization; connection between nature-inspired optimization and artificial intelligence; genetic and evolutionary algorithms; genetic programming; landscape analysis; multiobjective optimization; real-world applications; reinforcement learning; and theoretical aspects of nature-inspired optimization.

• EvoCOMNET Contributions.- Detection of DDoS Attacks via an Artificial Immune System-Inspired Multiobjective Evolutionary Algorithm.- Performance Evaluation of an Artificial Neural Network-Based Adaptive Antenna Array System.- Automatic Parameter Tuning with Metaheuristics of the AODV Routing Protocol for Vehicular Ad-Hoc Networks.- WiMAX Network Planning Using Adaptive-Population-Size Genetic Algorithm.- Markov Chain Models for Genetic Algorithm Based Topology Control in MANETs.- Particle Swarm Optimization for Coverage Maximization and Energy Conservation in Wireless Sensor Networks.- Efficient Load Balancing for a Resilient Packet Ring Using Artificial Bee Colony.- TCP Modification Robust to Packet Reordering in Ant Routing Networks.- Solving the Physical Impairment Aware Routing and Wavelength Assignment Problem in Optical WDM Networks Using a Tabu Search Based Hyper-Heuristic Approach.- A Generalized, Location-Based Model of Connections in Ad-Hoc Networks Improving the Performance of Ant Routing.- Using Code Bloat to Obfuscate Evolved Network Traffic.- ABC Supported Handoff Decision Scheme Based on Population Migration.- EvoENVIRONMENT Contributions.- A Hyper-Heuristic Approach for the Unit Commitment Problem.- Application of Genetic Programming Classification in an Industrial Process Resulting in Greenhouse Gas Emission Reductions.- Influence of Topology and Payload on CO2 Optimised Vehicle Routing.- Start-Up Optimisation of a Combined Cycle Power Plant with Multiobjective Evolutionary Algorithms.- EvoFIN Contributions.- A Study of Nature-Inspired Methods for Financial Trend Reversal Detection.- Outperforming Buy-and-Hold with Evolved Technical Trading Rules: Daily, Weekly and Monthly Trading.- Evolutionary Multi-stage Financial Scenario Tree Generation.- Evolving Dynamic Trade Execution Strategies Using Grammatical Evolution.- Modesty Is the Best Policy: Automatic Discovery of Viable Forecasting Goals in Financial Data.- Threshold Recurrent Reinforcement Learning Model for Automated Trading.- Active Portfolio Management from a Fuzzy Multi-objective Programming Perspective.- Evolutionary Monte Carlo Based Techniques for First Passage Time Problems in Credit Risk and Other Applications in Finance.- Calibrating the Heston Model with Differential Evolution.- Evolving Trading Rule-Based Policies.- EvoMUSART Contributions.- Evolving Artistic Styles through Visual Dialogues.- Graph-Based Evolution of Visual Languages.- Refinement Techniques for Animated Evolutionary Photomosaics Using Limited Tile Collections.- Generative Art and Evolutionary Refinement.- Aesthetic Learning in an Interactive Evolutionary Art System.- Comparing Aesthetic Measures for Evolutionary Art.- The Problem with Evolutionary Art Is ...- Learning to Dance through Interactive Evolution.- Jive: A Generative, Interactive, Virtual, Evolutionary Music System.- A Neural Network for Bass Functional Harmonization.- Combining Musical Constraints with Markov Transition Probabilities to Improve the Generation of Creative Musical Structures.- Dynamic Musical Orchestration Using Genetic Algorithms and a Spectro-Temporal Description of Musical Instruments.- Evolutionary Sound Synthesis: Rendering Spectrograms from Cellular Automata Histograms.- Sound Agents.- From Evolutionary Composition to Robotic Sonification.- Musical Composer Identification through Probabilistic and Feedforward Neural Networks.- EvoTRANSLOG Contributions.- Using an Evolutionary Algorithm to Discover Low CO2 Tours within a Travelling Salesman Problem.- A Genetic Algorithm for the Traveling Salesman Problem with Pickup and Delivery Using Depot Removal and Insertion Moves.- Fast Approximation Heuristics for Multi-Objective Vehicle Routing Problems.- Particle Swarm Optimization and an Agent-Based Algorithm for a Problem of Staff Scheduling.- A Math-Heuristic for the Multi-Level Capacitated Lot Sizing Problem with Carryover.
• (source: Nielsen Book Data)

Evolutionary computation (EC) techniques are e?cient, nature-inspired me- ods based on the principles of natural evolution and genetics. Due to their - ciency and simple underlying principles, these methods can be used for a diverse rangeofactivitiesincludingproblemsolving, optimization, machinelearningand pattern recognition. A large and continuously increasing number of researchers and professionals make use of EC techniques in various application domains. This volume presents a careful selection of relevant EC examples combined with a thorough examination of the techniques used in EC. The papers in the volume illustrate the current state of the art in the application of EC and should help and inspire researchers and professionals to develop e?cient EC methods for design and problem solving. All papers in this book were presented during EvoApplications 2010, which included a range of events on application-oriented aspects of EC. Since 1998, EvoApplications - formerly known as EvoWorkshops - has provided a unique opportunity for EC researchers to meet and discuss application aspects of EC and has been an important link between EC research and its application in a variety of domains. During these 12 years, new events have arisen, some have disappeared, whileothershavematuredtobecomeconferencesoftheirown, such as EuroGP in 2000, EvoCOP in 2004, and EvoBIO in 2007. And from this year, EvoApplications has become a conference as well.
(source: Nielsen Book Data)

• Genetic Programming.- Landscape Analysis.- Multiobjective Optimization.- Real-World Applications.- Reinforcement Learning.- Theoretical Aspects of Nature-Inspired Optimization.
• .
• (source: Nielsen Book Data)

This two-volume set LNCS 12269 and LNCS 12270 constitutes the refereed proceedings of the 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020, held in Leiden, The Netherlands, in September 2020. The 99 revised full papers were carefully reviewed and selected from 268 submissions. The topics cover classical subjects such as automated algorithm selection and configuration; Bayesian- and surrogate-assisted optimization; benchmarking and performance measures; combinatorial optimization; connection between nature-inspired optimization and artificial intelligence; genetic and evolutionary algorithms; genetic programming; landscape analysis; multiobjective optimization; real-world applications; reinforcement learning; and theoretical aspects of nature-inspired optimization.
(source: Nielsen Book Data)

• Digital Hardware Evolution.- An Online EHW Pattern Recognition System Applied to Sonar Spectrum Classification.- Design of Electronic Circuits Using a Divide-and-Conquer Approach.- Implementing Multi-VRC Cores to Evolve Combinational Logic Circuits in Parallel.- An Intrinsic Evolvable Hardware Based on Multiplexer Module Array.- Estimating Array Connectivity and Applying Multi-output Node Structure in Evolutionary Design of Digital Circuits.- Research on the Online Evaluation Approach for the Digital Evolvable Hardware.- Research on Multi-objective On-Line Evolution Technology of Digital Circuit Based on FPGA Model.- Evolutionary Design of Generic Combinational Multipliers Using Development.- Analog Hardware Evolution.- Automatic Synthesis of Practical Passive Filters Using Clonal Selection Principle-Based Gene Expression Programming.- Research on Fault-Tolerance of Analog Circuits Based on Evolvable Hardware.- Analog Circuit Evolution Based on FPTA-2.- Bio-inspired Systems.- Knowledge Network Management System with Medicine Self Repairing Strategy.- Design of a Cell in Embryonic Systems with Improved Efficiency and Fault-Tolerance.- Design on Operator-Based Reconfigurable Hardware Architecture and Cell Circuit.- Bio-inspired Systems with Self-developing Mechanisms.- Development of a Tiny Computer-Assisted Wireless EEG Biofeedback System.- Steps Forward to Evolve Bio-inspired Embryonic Cell-Based Electronic Systems.- Evolution of Polymorphic Self-checking Circuits.- Mechanical Hardware Evolution.- Sliding Algorithm for Reconfigurable Arrays of Processors.- System-Level Modeling and Multi-objective Evolutionary Design of Pipelined FFT Processors for Wireless OFDM Receivers.- Reducing the Area on a Chip Using a Bank of Evolved Filters.- Evolutionary Design.- Walsh Function Systems: The Bisectional Evolutional Generation Pattern.- Extrinsic Evolvable Hardware on the RISA Architecture.- Evolving and Analysing "Useful" Redundant Logic.- Adaptive Transmission Technique in Underwater Acoustic Wireless Communication.- Autonomous Robot Path Planning Based on Swarm Intelligence and Stream Functions.- Research on Adaptive System of the BTT-45 Air-to-Air Missile Based on Multilevel Hierarchical Intelligent Controller.- The Design of an Evolvable On-Board Computer.- Evolutionary Algorithms in Hardware Design.- Extending Artificial Development: Exploiting Environmental Information for the Achievement of Phenotypic Plasticity.- UDT-Based Multi-objective Evolutionary Design of Passive Power Filters of a Hybrid Power Filter System.- Designing Electronic Circuits by Means of Gene Expression Programming II.- Designing Polymorphic Circuits with Evolutionary Algorithm Based on Weighted Sum Method.- Robust and Efficient Multi-objective Automatic Adjustment for Optical Axes in Laser Systems Using Stochastic Binary Search Algorithm.- Minimization of the Redundant Sensor Nodes in Dense Wireless Sensor Networks.- Evolving in Extended Hamming Distance Space: Hierarchical Mutation Strategy and Local Learning Principle for EHW.- Hardware Implementation of Evolutionary Algorithms.- Adaptive and Evolvable Analog Electronics for Space Applications.- Improving Flexibility in On-Line Evolvable Systems by Reconfigurable Computing.- Evolutionary Design of Resilient Substitution Boxes: From Coding to Hardware Implementation.- A Sophisticated Architecture for Evolutionary Multiobjective Optimization Utilizing High Performance DSP.- FPGA-Based Genetic Algorithm Kernel Design.- Using Systolic Technique to Accelerate an EHW Engine for Lossless Image Compression.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 7th International Conference on Evolvable Systems, ICES 2007, held in Wuhan, China, in September 2007. The 41 revised full papers collected in this volume are organized in topical sections on digital hardware evolution, analog hardware evolution, bio-inspired systems, mechanical hardware evolution, evolutionary design, evolutionary algorithms in hardware design, and hardware implementation of evolutionary algorithms.
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• EvoCOMPLEX Contributions.- Coevolutionary Dynamics of Interacting Species.- Evolving Individual Behavior in a Multi-agent Traffic Simulator.- On Modeling and Evolutionary Optimization of Nonlinearly Coupled Pedestrian Interactions.- Revising the Trade-off between the Number of Agents and Agent Intelligence.- Sexual Recombination in Self-Organizing Interaction Networks.- Symbiogenesis as a Mechanism for Building Complex Adaptive Systems: A Review.- EvoGAMES Contributions.- Co-evolution of Optimal Agents for the Alternating Offers Bargaining Game.- Fuzzy Nash-Pareto Equilibrium: Concepts and Evolutionary Detection.- An Evolutionary Approach for Solving the Rubik's Cube Incorporating Exact Methods.- Evolution of Artificial Terrains for Video Games Based on Accessibility.- Evolving Behaviour Trees for the Commercial Game DEFCON.- Evolving 3D Buildings for the Prototype Video Game Subversion.- Finding Better Solutions to the Mastermind Puzzle Using Evolutionary Algorithms.- Towards a Generic Framework for Automated Video Game Level Creation.- Search-Based Procedural Content Generation.- Evolution of Grim Trigger in Prisoner Dilemma Game with Partial Imitation.- Evolving a Ms. PacMan Controller Using Grammatical Evolution.- Evolving Bot AI in UnrealTM.- Evolutionary Algorithm for Generation of Entertaining Shinro Logic Puzzles.- Social Learning Algorithms Reaching Nash Equilibrium in Symmetric Cournot Games.- Multiple Overlapping Tiles for Contextual Monte Carlo Tree Search.- EvoIASP Contributions.- A CNN Based Algorithm for the Automated Segmentation of Multiple Sclerosis Lesions.- A Hybrid Evolutionary Algorithm for Bayesian Networks Learning: An Application to Classifier Combination.- Towards Automated Learning of Object Detectors.- Markerless Multi-view Articulated Pose Estimation Using Adaptive Hierarchical Particle Swarm Optimisation.- Hand Posture Recognition Using Real-Time Artificial Evolution.- Comparing Cellular and Panmictic Genetic Algorithms for Real-Time Object Detection.- Bloat Free Genetic Programming versus Classification Trees for Identification of Burned Areas in Satellite Imagery.- Genetic Algorithms for Training Data and Polynomial Optimization in Colorimetric Characterization of Scanners.- New Genetic Operators in the Fly Algorithm: Application to Medical PET Image Reconstruction.- Chaotic Hybrid Algorithm and Its Application in Circle Detection.- Content-Based Image Retrieval of Skin Lesions by Evolutionary Feature Synthesis.- An Evolutionary Method for Model-Based Automatic Segmentation of Lower Abdomen CT Images for Radiotherapy Planning.- Evolution of Communicating Individuals.- Dynamic Data Clustering Using Stochastic Approximation Driven Multi-Dimensional Particle Swarm Optimization.- Automatic Synthesis of Associative Memories through Genetic Programming: A First Co-evolutionary Approach.- EvoINTELLIGENCE Contributions.- A Comparative Study between Genetic Algorithm and Genetic Programming Based Gait Generation Methods for Quadruped Robots.- Markerless Localization for Blind Users Using Computer Vision and Particle Swarm Optimization.- Particle Swarm Optimization for Feature Selection in Speaker Verification.- Scale- and Rotation-Robust Genetic Programming-Based Corner Detectors.- Self-organized and Evolvable Cognitive Architecture for Intelligent Agents and Multi-Agent Systems.- EvoNUM Contributions.- Investigating the Local-Meta-Model CMA-ES for Large Population Sizes.- Exploiting Evolution for an Adaptive Drift-Robust Classifier in Chemical Sensing.- Automatically Modeling Hybrid Evolutionary Algorithms from Past Executions.- Gaussian Adaptation Revisited - An Entropic View on Covariance Matrix Adaptation.- Parallel Genetic Algorithm on the CUDA Architecture.- A New Selection Ratio for Large Population Sizes.- Multi-Objective Probability Collectives.- Parallel Random Injection Differential Evolution.- Effect of Spatial Locality on an Evolutionary Algorithm for Multimodal Optimization.- A Directed Mutation Operator for Real Coded Genetic Algorithms.- Speedups between x70 and x120 for a Generic Local Search (Memetic) Algorithm on a Single GPGPU Chip.- Advancing Model-Building for Many-Objective Optimization Estimation of Distribution Algorithms.- Estimation Distribution Differential Evolution.- Design of Continuous Controllers Using a Multiobjective Differential Evolution Algorithm with Spherical Pruning.- Parameter Tuning of Evolutionary Algorithms: Generalist vs. Specialist.- EvoSTOC Contributions.- Memory Design for Constrained Dynamic Optimization Problems.- Multi-population Genetic Algorithms with Immigrants Scheme for Dynamic Shortest Path Routing Problems in Mobile Ad Hoc Networks.- Measuring Fitness Degradation in Dynamic Optimization Problems.- Handling Undefined Vectors in Expensive Optimization Problems.- Adaptive Noisy Optimization.- Noise Analysis Compact Genetic Algorithm.
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Evolutionary Computation (EC) techniques are e?cient, nature-inspired me- ods based on the principles of natural evolution and genetics. Due to their - ciency and simple underlying principles, these methods can be used for a diverse rangeofactivitiesincludingproblemsolving, optimization, machinelearningand pattern recognition. A large and continuously increasing number of researchers and professionals make use of EC techniques in various application domains. This volume presents a careful selection of relevant EC examples combined with a thorough examination of the techniques used in EC. The papers in the volume illustrate the current state of the art in the application of EC and should help and inspire researchers and professionals to develop e?cient EC methods for design and problem solving. All papers in this book were presented during EvoApplications 2010, which included a range of events on application-oriented aspects of EC. Since 1998, EvoApplications - formerly known as EvoWorkshops- has provided a unique opportunity for EC researchers to meet and discuss application aspects of EC and has been an important link between EC research and its application in a variety of domains. During these 12 years, new events have arisen, some have disappeared, whileothershavematuredtobecomeconferencesoftheirown, such as EuroGP in 2000, EvoCOP in 2004, and EvoBIO in 2007. And from this year, EvoApplications has become a conference as well.
(source: Nielsen Book Data)

• Identifying Regulatory Sites Using Neighborhood Species.- Genetic Programming and Other Machine Learning Approaches to Predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding Levels (%PPB) of Drugs.- Hypothesis Testing with Classifier Systems for Rule-Based Risk Prediction.- Robust Peak Detection and Alignment of nanoLC-FT Mass Spectrometry Data.- One-Versus-One and One-Versus-All Multiclass SVM-RFE for Gene Selection in Cancer Classification.- Understanding Signal Sequences with Machine Learning.- Targeting Differentially Co-regulated Genes by Multiobjective and Multimodal Optimization.- Modeling Genetic Networks: Comparison of Static and Dynamic Models.- A Genetic Embedded Approach for Gene Selection and Classification of Microarray Data.- Modeling the Shoot Apical Meristem in A. thaliana: Parameter Estimation for Spatial Pattern Formation.- Evolutionary Search for Improved Path Diagrams.- Simplifying Amino Acid Alphabets Using a Genetic Algorithm and Sequence Alignment.- Towards Evolutionary Network Reconstruction Tools for Systems Biology.- A Gaussian Evolutionary Method for Predicting Protein-Protein Interaction Sites.- Bio-mimetic Evolutionary Reverse Engineering of Genetic Regulatory Networks.- Tuning ReliefF for Genome-Wide Genetic Analysis.- Dinucleotide Step Parameterization of Pre-miRNAs Using Multi-objective Evolutionary Algorithms.- Amino Acid Features for Prediction of Protein-Protein Interface Residues with Support Vector Machines.- Predicting HIV Protease-Cleavable Peptides by Discrete Support Vector Machines.- Inverse Protein Folding on 2D Off-Lattice Model: Initial Results and Perspectives.- Virtual Error: A New Measure for Evolutionary Biclustering.- Characterising DNA/RNA Signals with Crisp Hypermotifs: A Case Study on Core Promoters.- Evaluating Evolutionary Algorithms and Differential Evolution for the Online Optimization of Fermentation Processes.- The Role of a Priori Information in the Minimization of Contact Potentials by Means of Estimation of Distribution Algorithms.- Classification of Cell Fates with Support Vector Machine Learning.- Reconstructing Linear Gene Regulatory Networks.- Individual-Based Modeling of Bacterial Foraging with Quorum Sensing in a Time-Varying Environment.- Substitution Matrix Optimisation for Peptide Classification.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2007, held in Valencia, Spain, April 2007. Coverage brings together experts in computer science with experts in bioinformatics and the biological sciences. It presents contributions on fundamental and theoretical issues along with papers dealing with different applications areas.
(source: Nielsen Book Data)

• Overall Design and Fundations.- Hybrid Multi-agent Systems: Integrating Swarming and BDI Agents.- An Analysis and Design Concept for Self-organization in Holonic Multi-agent Systems.- Design Patterns for Decentralised Coordination in Self-organising Emergent Systems.- Measuring Stigmergy: The Case of Foraging Ants.- Algorithms and Techniques.- Dynamic Decentralized Any-Time Hierarchical Clustering.- Behaviosites: A Novel Paradigm for Affecting Distributed Behavior.- Applications.- Programming Modular Robots with the TOTA Middleware.- ASOS: An Adaptive Self-organizing Protocol for Surveillance and Routing in Sensor Networks.- Towards the Control of Emergence by the Coordination of Decentralized Agent Activity for the Resource Sharing Problem.- Self-organization and Evolutionary Computing.- Reinforcement Learning for Online Control of Evolutionary Algorithms.- Greedy Cheating Liars and the Fools Who Believe Them.- Evolution and Hypercomputing in Global Distributed Evolvable Virtual Machines Environment.- A Decentralised Car Traffic Control System Simulation Using Local Message Propagation Optimised with a Genetic Algorithm.
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This book constitutes the thoroughly refereed post-proceedings of the 4th International Workshop on Engineering Self-Organising Applications, ESOA 2006, held in Hakodate, Japan in May 2006. This was an associated event of AAMAS 2006, the 5th International Joint Conference on Autonomous Agents and Multi-Agent Systems. The seven full papers presented together with six invited papers were carefully selected for inclusion in the book.
(source: Nielsen Book Data)

• Multiobjective Evolutionary Optimization.- A New Evolutionary Decision Theory for Many-Objective Optimization Problems.- A Multi-Objective Genetic Algorithm Based on Density.- Interplanetary Trajectory Optimization with Swing-Bys Using Evolutionary Multi-objective Optimization.- A Hybrid Evolutionary Multi-objective and SQP Based Procedure for Constrained Optimization.- Study on Application of Multi-Objective Differential Evolution Algorithm in Space Rendezvous.- The Multi-objective ITO Algorithms.- An Evolutionary Algorithm for Dynamic Multi-Objective TSP.- The Construction of Dynamic Multi-objective Optimization Test Functions.- An Effective Dynamical Multi-objective Evolutionary Algorithm for Solving Optimization Problems with High Dimensional Objective Space.- Evolutionary Algorithms and Operators.- Operator Adaptation in Evolutionary Programming.- A Comparison of GAs Using Penalizing Infeasible Solutions and Repairing Infeasible Solutions on Average Capacity Knapsack.- About the Limit Behaviors of the Transition Operators Associated with EAs.- Differential Evolution Algorithm Based on Simulated Annealing.- A Novel Memetic Algorithm for Global Optimization Based on PSO and SFLA.- Building on Success in Genetic Programming: Adaptive Variation and Developmental Evaluation.- A Granular Evolutionary Algorithm Based on Cultural Evolution.- A Self-adaptive Mutations with Multi-parent Crossover Evolutionary Algorithm for Solving Function Optimization Problems.- Evolutionary Optimization.- A Quantum Genetic Simulated Annealing Algorithm for Task Scheduling.- Optimized Research of Resource Constrained Project Scheduling Problem Based on Genetic Algorithms.- An Evolutionary Agent System for Mathematical Programming.- Agent-Based Coding GA and Application to Combat Modeling and Simulation.- A Two-Stage Genetic Algorithm for the Multi-multicast Routing.- A Novel Lower-Dimensional-Search Algorithm for Numerical Optimization.- Performance Evaluation of Three Kinds of Quantum Optimization.- An Efficient Multilevel Algorithm for Inverse Scattering Problem.- Evolutionary Learning.- A New Evolutionary Neural Network and Its Application for the Extraction of Vegetation Anomalies.- Dynamic System Evolutionary Modeling: The Case of SARS in Beijing.- An Tableau Automated Theorem Proving Method Using Logical Reinforcement Learning.- Gene Expression Programming with DAG Chromosome.- Neural Networks.- Zhang Neural Network for Online Solution of Time-Varying Sylvester Equation.- A Global Optimization Algorithm Based on Novel Interval Analysis for Training Neural Networks.- Approximate Interpolation by Neural Networks with the Inverse Multiquadric Functions.- Decomposition Mixed Pixel of Remote Sensing Image Based on Tray Neural Network Model.- A Novel Kernel Clustering Algorithm Based Selective Neural Network Ensemble Model for Economic Forecasting.- An Evolutionary Neural Network Based Tracking Control of a Human Arm in the Sagittal Plane.- Ant Colony, Particle Swarm Optimization and Artificial Immune Systems.- New Ant Colony Optimization for Optimum Multiuser Detection Problem in DS-CDMA Systems.- A Fast Particle Swarm Optimization Algorithm with Cauchy Mutation and Natural Selection Strategy.- Fast Multi-swarm Optimization with Cauchy Mutation and Crossover Operation.- Particle Swarm Optimization Using Levy Probability Distribution.- Re-diversification Based Particle Swarm Algorithm with Cauchy Mutation.- An Improved Multi-Objective Particle Swarm Optimization Algorithm.- Dynamic Population Size Based Particle Swarm Optimization.- An Improved Particle Swarm Optimization for Data Streams Scheduling on Heterogeneous Cluster.- A Steepest Descent Evolution Immune Algorithm for Multimodal Function Optimization.- A Hybrid Clonal Selection Algorithm Based on Multi-parent Crossover and Chaos Search.- Pattern Recognition.- Spatial Clustering Method Based on Cloud Model and Data Field.- Diversity Analysis of Information Pattern and Information Clustering Algorithm.- Instant Message Clustering Based on Extended Vector Space Model.- Continuous K-Nearest Neighbor Queries for Moving Objects.- Texture Classification of Aerial Image Based on Bayesian Networks with Hidden Nodes.- Human Motion Recognition Based on Hidden Markov Models.- Data Mining.- Parameter Setting for Evolutionary Latent Class Clustering.- Automatic Data Mining by Asynchronous Parallel Evolutionary Algorithms.- Texture Image Retrieval Based on Contourlet Coefficient Modeling with Generalized Gaussian Distribution.- Heterogeneous Spatial Data Mining Based on Grid.- A Clustering Scheme for Large High-Dimensional Document Datasets.- Intelligent Systems.- Self-tuning PID Control of Hydro-turbine Governor Based on Genetic Neural Networks.- Adaptive Rate Selection Scheme Based on Intelligent Learning Algorithm in Wireless LANs.- The Research on Generic Project Risk Element Network Transmission Parallel Computing Model.- On the Performance of Metamodel Assisted MOEA/D.- A High Precision OGC Web Map Service Retrieval Based on Capability Aware Spatial Search Engine.- Analysis of the Performance of Balance of Digital Multi-value Based on Chebyshev Chaotic Sequence.- The Transformation Between Fuzzy Cognitive Maps and a Class of Simplified Dynamical Cognitive Networks.- Evolutionary Design.- Cryptanalysis of Two-Round DES Using Genetic Algorithms.- A Novel Artistic Image Generation Technique: Making Relief Effects Through Evolution.- Data Genome: An Abstract Model for Data Evolution.- Intrinsic Evolution of Frequency Splitter with a New Analog EHW Platform.- Towards the Role of Heuristic Knowledge in EA.- Using Instruction Matrix Based Genetic Programming to Evolve Programs.- Fuzzy Pattern Rule Induction for Information Extraction.- An Orthogonal and Model Based Multiobjective Genetic Algorithm for LEO Regional Satellite Constellation Optimization.
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This book constitutes the refereed proceedings of the Second International Symposium on Intelligence Computation and Applications, ISICA 2007, held in Wuhan, China, in September 2007. The 71 revised full papers cover such topics as evolutionary computation, evolutionary learning, neural networks, swarms, pattern recognition, and data mining.
(source: Nielsen Book Data)

• Heuristics I.- Alternative Solution Representations for the Job Shop Scheduling Problem in Ant Colony Optimisation.- Analyzing the Role of "Smart" Start Points in Coarse Search-Greedy Search.- Concealed Contributors to Result Quality - The Search Process of Ant Colony System.- Ants Guide Future Pilots.- Complex Systems I.- Information Transfer by Particles in Cellular Automata.- An Artificial Development Model for Cell Pattern Generation.- Rounds Effect in Evolutionary Games.- Modelling Architectural Visual Experience Using Non-linear Dimensionality Reduction.- An Evolutionary Benefit from Misperception in Foraging Behaviour.- Simulated Evolution of Discourse with Coupled Recurrent Networks.- How Different Hierarchical Relationships Impact Evolution.- A Dual Phase Evolution Model of Adaptive Radiation in Landscapes.- Biological Systems I.- Directed Evolution of an Artificial Cell Lineage.- An Integrated QAP-Based Approach to Visualize Patterns of Gene Expression Similarity.- Complement-Based Self-Replicated, Self-Assembled Systems (CBSRSAS).- Self-maintained Movements of Droplets with Convection Flow.- Structural Circuits and Attractors in Kauffman Networks.- The Effects of Learning on the Roles of Chance, History and Adaptation in Evolving Neural Networks.- Unsupervised Acoustic Classification of Bird Species Using Hierarchical Self-organizing Maps.- The Prisoner's Dilemma with Image Scoring on Networks: How Does a Player's Strategy Depend on Its Place in the Social Network?.- Heuristics II.- Population-Based Ant Colony Optimisation for Multi-objective Function Optimisation.- Mechanisms for Evolutionary Reincarnation.- An Evolutionary Algorithm with Spatially Distributed Surrogates for Multiobjective Optimization.- Examining Dissimilarity Scaling in Ant Colony Approaches to Data Clustering.- Complex Systems II.- A Framework for the Co-evolution of Genes, Proteins and a Genetic Code Within an Artificial Chemistry Reaction Set.- In-Formation Flocking: An Approach to Data Visualization Using Multi-agent Formation Behavior.- A Principled Approach to Swarm-Based Wall-Building.- Pattern Extraction Improves Automata-Based Syntax Analysis in Songbirds.- Heuristics III.- A Modified Strategy for the Constriction Factor in Particle Swarm Optimization.- A Differential Evolution Variant of NSGA II for Real World Multiobjective Optimization.- Investigating a Hybrid Metaheuristic for Job Shop Rescheduling.- Enhancements to Extremal Optimisation for Generalised Assignment.- Biological Systems II.- Identification of Marker Genes Discriminating the Pathological Stages in Ovarian Carcinoma by Using Support Vector Machine and Systems Biology.- Ancestral DNA Sequence Reconstruction Using Recursive Genetic Algorithms.
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This book constitutes the refereed proceedings of the Third Australian Conference on Artificial Life, ACAL 2007, held in Gold Coast, Australia, in December 2007. The 34 revised full papers presented were carefully reviewed and selected from 70 submissions. Research in Alife covers the main areas of biological behaviour as a metaphor for computational models, computational models that reproduce/duplicate a biological behaviour, and computational models to solve biological problems.
(source: Nielsen Book Data)