%{search_type} search results

• 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.
• (source: Nielsen Book Data)

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)

• 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.
• (source: Nielsen Book Data)

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.
• (source: Nielsen Book Data)

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.
• (source: Nielsen Book Data)

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)

• 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.
• (source: Nielsen Book Data)

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)

• 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.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• 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. .

• 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. .
(source: Nielsen Book Data)

• 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)