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• 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.
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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.
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• 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.
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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.
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• 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.
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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.
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• 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.
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• 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.
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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.
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• 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.
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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.
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• 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.
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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).
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• 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.
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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.
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• 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.
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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.
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• 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.
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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.
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• 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.
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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.
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• 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.
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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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