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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.
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• 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)