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• Chapter 1. Recent neuro-fuzzy approaches for feature selection and classification
• Chapter 2. An approach to license plate recognition system using neural network
• Chapter 3. Intuitionistic fuzzy time series forecasting based on dual hesitant fuzzy set for stock market: DHFS-based IFTS model for stock market
• Chapter 4. Design and implementation of an intelligent traffic management system: a neural approach
• Chapter 5. DNA fragment assembly using quantum-inspired genetic algorithm
• Chapter 6. Effective prevention and reduction in the rate of accidents using internet of things and data analytics
• Chapter 7. Nature-inspired algorithms for bi-criteria parallel machine scheduling
• Chapter 8. Hybrid honey bees meta-heuristic for benchmark data classification
• Chapter 9. Guided search-based multi-objective evolutionary algorithm for grid workflow scheduling
• Chapter 10. An optimal configuration of sensitive parameters of PSO applied to textual clustering
• Chapter 11. An improved hybridized evolutionary algorithm based on rules for local sequence alignment
• Chapter 12. Bi-objective supply chain optimization with supplier selection
• Chapter 13. Overview and optimized design for energy recovery patents applied to hydraulic systems
• Chapter 14. Wireless robotics networks for search and rescue in underground mines: taxonomy and open issues
• Chapter 15. Solving job scheduling problem in computational grid systems using a hybrid algorithm
• Chapter 16. An enhanced clustering method for image segmentation

Modern optimization approaches have attracted an increasing number of scientists, decision makers, and researchers. As new issues in this field emerge, different optimization methodologies must be developed and implemented. Exploring Critical Approaches of Evolutionary Computation is a vital scholarly publication that explores the latest developments, methods, approaches, and applications of evolutionary models in a variety of fields. It also emphasizes evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, genetic programming, and related fields such as swarm intelligence and other evolutionary computation techniques. Highlighting a range of pertinent topics such as neural networks, data mining, and data analytics, this book is designed for IT developers, IT theorists, computer engineers, researchers, practitioners, and upper-level students seeking current research on enhanced information exchange methods and practical aspects of computational systems.
(source: Nielsen Book Data)

Modern optimization approaches have attracted an increasing number of scientists, decision makers, and researchers. As new issues in this field emerge, different optimization methodologies must be developed and implemented. Exploring Critical Approaches of Evolutionary Computation is a vital scholarly publication that explores the latest developments, methods, approaches, and applications of evolutionary models in a variety of fields. It also emphasizes evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, genetic programming, and related fields such as swarm intelligence and other evolutionary computation techniques. Highlighting a range of pertinent topics such as neural networks, data mining, and data analytics, this book is designed for IT developers, IT theorists, computer engineers, researchers, practitioners, and upper-level students seeking current research on enhanced information exchange methods and practical aspects of computational systems.
(source: Nielsen Book Data)

• Preface.- Introduction.- Multilevel segmentation in digital images.- Multi-Circle detection on images.- Template matching.- Motion estimation.- Photovoltaic cell design.- Parameter identification of induction motors.- White blood cells Detection in images.- Estimation of view transformations in images.- Filter Design.
• (source: Nielsen Book Data)

This book compares the performance of various evolutionary computation (EC) techniques when they are faced with complex optimization problems extracted from different engineering domains. Particularly focusing on recently developed algorithms, it is designed so that each chapter can be read independently. Several comparisons among EC techniques have been reported in the literature, however, they all suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. In each chapter, a complex engineering optimization problem is posed, and then a particular EC technique is presented as the best choice, according to its search characteristics. Lastly, a set of experiments is conducted in order to compare its performance to other popular EC methods.
(source: Nielsen Book Data)

• Optimum Tracking in Dynamic Environments.- Explicit Memory Schemes for Evolutionary Algorithms in Dynamic Environments.- Particle Swarm Optimization in Dynamic Environments.- Evolution Strategies in Dynamic Environments.- Orthogonal Dynamic Hill Climbing Algorithm: ODHC.- Genetic Algorithms with Self-Organizing Behaviour in Dynamic Environments.- Learning and Anticipation in Online Dynamic Optimization.- Evolutionary Online Data Mining: An Investigation in a Dynamic Environment.- Adaptive Business Intelligence: Three Case Studies.- Evolutionary Algorithms for Combinatorial Problems in the Uncertain Environment of the Wireless Sensor Networks.- Approximation of Fitness Functions.- Individual-based Management of Meta-models for Evolutionary Optimization with Application to Three-Dimensional Blade Optimization.- Evolutionary Shape Optimization Using Gaussian Processes.- A Study of Techniques to Improve the Efficiency of a Multi-Objective Particle Swarm Optimizer.- An Evolutionary Multi-objective Adaptive Meta-modeling Procedure Using Artificial Neural Networks.- Surrogate Model-Based Optimization Framework: A Case Study in Aerospace Design.- Handling Noisy Fitness Functions.- Hierarchical Evolutionary Algorithms and Noise Compensation via Adaptation.- Evolving Multi Rover Systems in Dynamic and Noisy Environments.- A Memetic Algorithm Using a Trust-Region Derivative-Free Optimization with Quadratic Modelling for Optimization of Expensive and Noisy Black-box Functions.- Genetic Algorithm to Optimize Fitness Function with Sampling Error and its Application to Financial Optimization Problem.- Search for Robust Solutions.- Single/Multi-objective Inverse Robust Evolutionary Design Methodology in the Presence of Uncertainty.- Evolving the Tradeoffs between Pareto-Optimality and Robustness in Multi-Objective Evolutionary Algorithms.- Evolutionary Robust Design of Analog Filters Using Genetic Programming.- Robust Salting Route Optimization Using Evolutionary Algorithms.- An Evolutionary Approach For Robust Layout Synthesis of MEMS.- A Hybrid Approach Based on Evolutionary Strategies and Interval Arithmetic to Perform Robust Designs.- An Evolutionary Approach for Assessing the Degree of Robustness of Solutions to Multi-Objective Models.- Deterministic Robust Optimal Design Based on Standard Crowding Genetic Algorithm.
• (source: Nielsen Book Data)

This book compiles recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The book is motivated by the fact that some degree of uncertainty is inevitable in characterizing any realistic engineering systems. Discussion includes representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums.
(source: Nielsen Book Data)

• Introduction
• Reconfigurable Hardware
• Evolutionary Algorithms
• Evolvable Hardware
• Towards Evolvable Components
• Evolvable Computational Machines
• An Evolvable Component for Image-Pre-processing
• Virtual Reconfigurable Devices
• Concluding Statements
• References
• Index.
• (source: Nielsen Book Data)

This book deals with adaptive evolvable systems operating in a changing environment. The proposed component approach for the design of evolvable systems, the mathematical theory of evolvable machines, and the idea of virtual reconfigurable circuits have opened a way towards a better understanding of evolvable systems and to the design of more adaptive, competitive and innovative engineering products. In particular, topics such as the computational power of evolvable systems, the design of complex human-competitive digital circuits and the implementation of a one-clock partial reconfiguration in a common field-programmable gate array are introduced, investigated and implemented. The application domain is adaptive image preprocessing.
(source: Nielsen Book Data)

• [I]. Engineering and real-world applications: 1. A comparison of different many-objective optimization algorithms for energy system optimization / Tobias Rodemann
• 2. Design of powered floor systems for mobile robots with differential evolution / Eric Medvet, Stefano Seriani, Alberto Bartoli, and Paolo Gallina
• 3. Solving the multi-objective flexible job-shop scheduling problem with alternative recipes for a chemical production process / Piotr Dziurzanski, Shuai Zhao, Jerry Swan, Leandro Soares Indrusiak, Sebastian Scholze, and Karl Krone
• 4. Quantifying the effects of increasing user choice in MAP-elites applied to a workforce scheduling and routing problem / Neil Urquhart, Emma Hart, and William Hutcheson
• 5. A hybrid multi-objective differential evolution approach to stator winding optimization / André M. Silva, Fernando J.T.E. Ferreira, and Carlos Henggeler Antunes
• 6. GA-Novo : de novo peptide sequencing via tandem mass spectrometry using genetic algorithm / Samaneh Azari, Bing Xue, Mengjie Zhang, and Lifeng Peng
• 7. Ant colony optimization for optimized operation scheduling of combined heat and power plants / Johannes Mast, Stefan Rädle, Joachim Gerlach, and Oliver Bringmann
• 8. A flexible dissimilarity measure for active and passive 3D structures and its application in the fitness-distance analysis / Maciej Komosinski and Agnieszka Mensfelt.

• [II]. Games: 9. Free form evolution for Angry Birds level generation / Laura Calle, Juan J. Merelo, Antonio Mora-García, and José-Mario García-Valdez
• 10. Efficient online hyperparameter adaptation for deep reinforcement learning / Yinda Zhou, Weiming Liu, and Bin Li
• 11. GAMER : a genetic algorithm with motion encoding reuse for action-adventure video games / Tasos Papagiannis, Georgios Alexandridis, and Andreas Stafylopatis
• 12. Effects of input addition in learning for adaptive games : towards learning with structural changes / Iago Bonnici, Abdelkader Gouaïch, and Fabien Michel.

• [III]. General: 13. Supporting medical decisions for treating rare diseases through genetic programming / Illya Bakurov, Mauro Castelli, Leonardo Vanneschi, and Maria João Freitas
• 14. Evolutionary successful strategies in a transparent iterated prisoner's dilemma / Anton M. Unakafov, Thomas Schultze, Igor Kagan, Sebastian Moeller, Alexander Gail, Stefan Treue, Stephan Eule, and Fred Wolf
• 15. Evolutionary algorithms for the design of quantum protocols / Walter Krawec, Stjepan Picek, and Domagoj Jakobovic
• 16. Evolutionary computation techniques for constructing SAT-based attacks in algebraic cryptanalysis / Artem Pavlenko, Alexander Semenov, and Vladimir Ulyantsev
• 17. On the use of evolutionary computation for in-silico medicine : modelling sepsis via evolving continuous petri nets / Ahmed Hallawa, Elisabeth Zechendorf, Yi Song, Anke Schmeink, Arne Peine, Lukas Marin, Gerd Ascheid, and Guido Dartmann
• 18. A cultural algorithm for determining similarity values between users in recommender systems / Kalyani Selvarajah, Ziad Kobti, and Mehdi Kargar.

• [IV]. Image and signal processing: 19. Optimizing the C index using a canonical genetic algorithm / Thomas A. Runkler and James C. Bezdek
• 20. Memetic evolution of classification ensembles / Szymon Piechaczek, Michal Kawulok, and Jakub Nalepa
• 21. Genetic programming for feature selection and feature combination in salient object detection / Shima Afzali, Harith Al-Sahaf, Bing Xue, Christopher Hollitt, and Mengjie Zhang
• 22. Variable-length representation for EC-based feature selection in high-dimensional data / N.D. Cilia, C. De Stefano, F. Fontanella, and A. Scotto di Freca.

• [V]. Life sciences: 23. A knowledge based differential evolution algorithm for protein structure prediction / Pedro H. Narloch and Márcio Dorn
• 24. A biased random key genetic algorithm with local search chains for molecular docking / Pablo F. Leonhart and Márcio Dorn
• 25. Self-sustainability challenges of plants colonization strategies in virtual 3D environments / Kevin Godin-Dubois, Sylvain Cussat-Blanc, and Yves Duthen.

• [VI]. Networks and distributed systems: 26. Early detection of Botnet activities using grammatical evolution / Selim Yilmaz and Sevil Sen
• 27. Exploring concurrent and stateless evolutionary algorithms / Juan J. Merelo, J.L.J. Laredo, Pedro A. Castillo, José-Mario García-Valdez, and Sergio Rojas-Galeano
• 28. Evolving trust formula to evaluate data trustworthiness in VANETs using genetic programming / Mehmet Aslan and Sevil Sen
• 29. A matheuristic for green and robust 5G virtual network function placement / Thomas Bauschert, Fabio D'Andreagiovanni, Andreas Kassler, and Chenghao Wang
• 30. Prolong the network lifetime of wireless underground sensor networks by optimal relay node placement / Nguyen Thi Tam, Huynh Thi Thanh Binh, Tran Huy Hung, Dinh Anh Dung, and Le Trong Vinh.

• [VII]. Neuroevolution and data analytics: 31. The evolution of self-taught neural networks in a multi-agent environment / Nam Le, Anthony Brabazon, and Michael O'Neill
• 32. Coevolution of generative adversarial networks / Victor Costa, Nuno Lourenço, and Penousal Machado
• 33. Evolving recurrent neural networks for time series data prediction of coal plant parameters / AbdElRahman ElSaid, Steven Benson, Shuchita Patwardhan, David Stadem, and Travis Desell
• 34. Improving NeuroEvolution efficiency by surrogate model-based optimization with phenotypic distance kernels / Jörg Stork, Martin Zaefferer, and Thomas Bartz-Beielstein.

• [VIII]. Numerical optimization : theory, benchmarks and applications: 35. Compact optimization algorithms with re-sampled inheritance / Giovanni Iacca and Fabio Caraffini
• 36. Particle swarm optimization : understanding order-2 stability guarantees / Christopher W. Cleghorn
• 37. Fundamental flowers : evolutionary discovery of coresets for classification / Pietro Barbiero and Alberto Tonda.

This book constitutes the refereed proceedings of the 22nd International Conference on Applications of Evolutionary Computation, EvoApplications 2019, held in Leipzig, Germany, in April 2019, co-located with the Evo*2019 events EuroGP, EvoCOP and EvoMUSART. The 44 revised full papers presented were carefully reviewed and selected from 66 submissions. They were organized in topical sections named: Engineering and Real World Applications; Games; General; Image and Signal Processing; Life Sciences; Networks and Distributed Systems; Neuroevolution and Data Analytics; Numerical Optimization: Theory, Benchmarks, and Applications; Robotics.
(source: Nielsen Book Data)

• Applications of Evolutionary Computation
• Analysis of Evolutionary Computation Methods: Theory, Empirics, and Real-World Applications
• Computational Intelligence for Sustainability
• Evolutionary Computation in Edge, Fog, and Cloud Computing
• Evolutionary Machine Learning
• Machine Learning and AI in Digital Healthcare and Personalized Medicine
• Resilient Bio-Inspired Algorithms
• Soft Computing applied to Games
• Surrogate-Assisted Evolutionary Optimisation.

This book constitutes the refereed proceedings of the 25th International Conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of Evo*2023, in April 2023, co-located with the Evo*2023 events EuroGP, EvoCOP, and EvoMUSART. The EuroGP focused on the technique of genetic programming, EvoCOP targeted evolutionary computation in combinatorial optimization, and EvoMUSART was dedicated to evolved and bio-inspired music, sound, art, and design. The EvoApplications 2023 presents papers on the different areas: Analysis of Evolutionary Computation Methods: Theory, Empirics, and Real-World Applications, Applications of Bio-inspired Techniques on Social Networks, Evolutionary Computation in Edge, Fog, and Cloud Computing, Evolutionary Computation in Image Analysis, Signal Processing, and Pattern Recognition and others.

• pt. 1. EvoApplications 2011 : EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC, Torino, Italy, April 27-29, 2011, proceedings
• pt. 2. EvoApplications 2011 : EvoCOMNET, EvoFIN, EvoHOT, EvoMUSART, EvoSTIM, and EvoTRANSLOG, Torino, Italy, April 27-29, 2011, proceedings.

This book constitutes the refereed proceedings of the International Conference on the Applications of Evolutionary Computation, EvoApplications 2011, held in Torino, Italy, in April 2011 colocated with the Evo 2011 events. Thanks to the large number of submissions received, the proceedings for EvoApplications 2011 are divided across two volumes (LNCS 6624 and 6625). The present volume contains contributions for EvoComnet, EvoFin, EvoIhot, EvoMusart, EvoStim, and EvoTransloc. The 51 revised full papers presented were carefully reviewed and selected from numerous submissions. This volume presents an overview about the latest research in EC. Areas where evolutionary computation techniques have been applied range from telecommunication networks to complex systems, finance and economics, games, image analysis, evolutionary music and art, parameter optimization, scheduling, and logistics. These papers may provide guidelines to help new researchers tackling their own problem using EC.
(source: Nielsen Book Data)

• PART I - Introduction to the Concepts of Bioinformatics and Evolutionary Computation
• Chapter 1. An Introduction to Bioinformatics for Computer Scientists By David W. Corne and Gary B. Fogel
• Chapter 2. An Introduction to Evolutionary Computation for Biologists By Gary B. Fogel and David W. Corne PART II - Sequence and Structure Alignment
• Chapter 3. Determining Genome Sequences from Experimental Data Using Evolutionary Computation By Jacek Blazewic and Marta Kasprzak
• Chapter 4. Protein Structure Alignment Using Evolutionary Computation By Joseph D. Szustakowski and Zhipeng Weng
• Chapter 5. Using Genetic Algorithms for Pairwise and Multiple Sequence Alignments By Cedric Notredame PART III - Protein Folding
• Chapter 6. On the Evolutionary Search for Solutions to the Protein Folding Problem By Garrison W. Greenwood and Jae-Min Shin
• Chapter 7. Toward Effective Polypeptide Structure Prediction with Parallel Fast Messy Genetic Algorithms By Gary B. Lamont and Laurence D. Merkle
• Chapter 8. Application of Evolutionary Computation to Protein Folding with Specialized Operators By Steffen Schulze-Kremer PART IV - Machine Learning and Inference
• Chapter 9. Identification of Coding Regions in DNA Sequences Using Evolved Neural Networks By Gary B. Fogel, Kumar Chellapilla, and David B. Fogel
• Chapter 10. Clustering Microarray Data with Evolutionary Algorithms By Emanuel Falkenauer and Arnaud Marchand
• Chapter 11. Evolutionary Computation and Fractal Visualization of Sequence Data By Dan Ashlock and Jim Golden
• Chapter 12. Identifying Metabolic Pathways and Gene Regulation Networks with Evolutionary Algorithms By Junji Kitagawa and Hitoshi Iba
• Chapter 13. Evolutionary Computational Support for the Characterization of Biological Systems By Bogdan Filipic and Janez Strancar PART V - Feature Selection
• Chapter 14. Discovery of Genetic and Environmental Interactions in Disease Data Using Evolutionary Computation By Laetitia Jourdan, Clarisse Dhaenens[AQ2], and El-Ghazali Talbi
• Chapter 15. Feature Selection Methods Based on Genetic Algorithms for in Silico Drug Design By Mark J. Embrechts, Muhsin Ozdemir, Larry Lockwood, Curt Breneman, Kristin Bennet, Dirk Devogelaere, and Marcel Rijkaert
• Chapter 16. Interpreting Analytical Spectra with Evolutionary Computation By Jem J. Rowland Appendix: Internet Resources for Bioinformatics Data and Tools.
• (source: Nielsen Book Data)

Bioinformatics has never been as popular as it is today. The genomics revolution is generating so much data in such rapid succession that it has become difficult for biologists to decipher. In particular, there are many problems in biology that are too large to solve with standard methods. Researchers in evolutionary computation (EC) have turned their attention to these problems. They understand the power of EC to rapidly search very large and complex spaces and return reasonable solutions. While these researchers are increasingly interested in problems from the biological sciences, EC and its problem-solving capabilities are generally not yet understood or applied in the biology community. This book offers a definitive resource to bridge the computer science and biology communities. Gary Fogel and David Corne, well-known representatives of these fields, introduce biology and bioinformatics to computer scientists, and evolutionary computation to biologists and computer scientists unfamiliar with these techniques. The fourteen chapters that follow are written by leading computer scientists and biologists who examine successful applications of evolutionary computation to various problems in the biological sciences. It describes applications of EC to bioinformatics in a wide variety of areas including DNA sequencing, protein folding, gene and protein classification, drug targeting, drug design, data mining of biological databases, and biodata visualization. It offers industrial and academic researchers in computer science, biology, and bioinformatics an important resource for applying evolutionary computation. It includes a detailed appendix of biological data resources.
(source: Nielsen Book Data)

• Basics.- Research in Evolutionary Computation.- The New Experimentalism.- Statistics for Computer Experiments.- Optimization Problems.- Designs for Computer Experiments.- Search Algorithms.- Results and Perspectives.- Comparison.- Understanding Performance.- Summary and Outlook.
• (source: Nielsen Book Data)

This book introduces the new experimentalism in evolutionary computation, providing tools to understand algorithms and programs and their interaction with optimization problems. It develops and applies statistical techniques to analyze and compare modern search heuristics such as evolutionary algorithms and particle swarm optimization. The book bridges the gap between theory and experiment by providing a self-contained experimental methodology and many examples.
(source: Nielsen Book Data)

• 1. Introduction and Scientific Goals
• 2. State of the Art: Grammatical Inference
• 3. State of the Art: Genetic Algorithms and Premature Convergence
• 4. Genetic Algorithms and Grammatical Inference
• 5. Performance Analysis of Genetic Algorithm for Grammatical Inference
• 6. Applications of Grammatical Inference Methods and Future Development.
• (source: Nielsen Book Data)

State of the Art on Grammatical Inference Using Evolutionary Method presents an approach for grammatical inference (GI) using evolutionary algorithms. Grammatical inference deals with the standard learning procedure to acquire grammars based on evidence about the language. It has been extensively studied due to its high importance in various fields of engineering and science. The book's prime purpose is to enhance the current state-of-the-art of grammatical inference methods and present new evolutionary algorithms-based approaches for context free grammar induction. The book's focus lies in the development of robust genetic algorithms for context free grammar induction. The new algorithms discussed in this book incorporate Boolean-based operators during offspring generation within the execution of the genetic algorithm. Hence, the user has no limitation on utilizing the evolutionary methods for grammatical inference.
(source: Nielsen Book Data)

• Preface
• Chapter 1. Introduction to Nature-inspired Algorithms
• Chapter 2. Ant Colony Optimizer: Theory, Literature Review, and Application in AUV Path Planning.-Chapter 3. Ant Lion Optimizer: Theory, Literature Review, and Application in Multi-layer Perceptron Neural Network
• Chapter 4. Dragonfly Algorithm: Theory, Literature Review, and Application in Feature Selection
• Chapter 5. Genetic Algorithm: Theory, Literature Review, and Application in Image Reconstruction etc.

This book covers the conventional and most recent theories and applications in the area of evolutionary algorithms, swarm intelligence, and meta-heuristics. Each chapter offers a comprehensive description of a specific algorithm, from the mathematical model to its practical application. Different kind of optimization problems are solved in this book, including those related to path planning, image processing, hand gesture detection, among others. All in all, the book offers a tutorial on how to design, adapt, and evaluate evolutionary algorithms. Source codes for most of the proposed techniques have been included as supplementary materials on a dedicated webpage.

• Introduction.- Optimization.- Metaheuristic optimization.- Image processing.- Image Segmentation using metaheuristics.- Multilevel thresholding for image segmentation based on metaheuristic Algorithms.- Otsu's between class variance and the tree seed algorithm.- Image segmentation using Kapur's entropy and a hybrid optimization algorithm.- Tsallis entropy for image thresholding.- Image segmentation with minimum cross entropy.- Fuzzy entropy approaches for image segmentation.- Image segmentation by gaussian mixture.- Image segmentation as a multiobjective optimization problem.- Clustering algorithms for image segmentation.- Contextual information in image thresholding.
• (source: Nielsen Book Data)

This book presents a study of the most important methods of image segmentation and how they are extended and improved using metaheuristic algorithms. The segmentation approaches selected have been extensively applied to the task of segmentation (especially in thresholding), and have also been implemented using various metaheuristics and hybridization techniques leading to a broader understanding of how image segmentation problems can be solved from an optimization perspective. The field of image processing is constantly changing due to the extensive integration of cameras in devices; for example, smart phones and cars now have embedded cameras. The images have to be accurately analyzed, and crucial pre-processing steps, like image segmentation, and artificial intelligence, including metaheuristics, are applied in the automatic analysis of digital images. Metaheuristic algorithms have also been used in various fields of science and technology as the demand for new methods designed to solve complex optimization problems increases. This didactic book is primarily intended for undergraduate and postgraduate students of science, engineering, and computational mathematics. It is also suitable for courses such as artificial intelligence, advanced image processing, and computational intelligence. The material is also useful for researches in the fields of evolutionary computation, artificial intelligence, and image processing.
(source: Nielsen Book Data)

• Chapter 1. The Science of Biogeography 1 1.1. Introduction 1 1.2. Island biogeography 3 1.3. Influence factors for biogeography 6
• Chapter 2. Biogeography and Biological Optimization 11 2.1. A mathematical model of biogeography 11 2.2. Biogeography as an optimization process 16 2.3. Biological optimization 19 2.3.1. Genetic algorithms 19 2.3.2. Evolution strategies 20 2.3.3. Particle swarm optimization 21 2.3.4. Artificial bee colony algorithm 22 2.4. Conclusion 23
• Chapter 3. A Basic BBO Algorithm 25 3.1. BBO definitions and algorithm 25 3.1.1. Migration 26 3.1.2. Mutation 27 3.1.3. BBO implementation 27 3.2. Differences between BBO and other optimization algorithms 35 3.2.1. BBO and genetic algorithms 35 3.2.2. BBO and other algorithms 36 3.3. Simulations 37 3.4. Conclusion 44
• Chapter 4. BBO Extensions 45 4.1. Migration curves 45 4.2. Blended migration 49 4.3. Other approaches to BBO 51 4.4. Applications 56 4.5. Conclusion 59
• Chapter 5. BBO as a Markov Process 61 5.1. Markov definitions and notations 61 5.2. Markov model of BBO 72 5.3. BBO convergence 79 5.4. Markov models of BBO extensions 90 5.5. Conclusions 99
• Chapter 6. Dynamic System Models of BBO 103 6.1. Basic notation 103 6.2. Dynamic system models of BBO 105 6.3. Applications to benchmark problems 119 6.4. Conclusions 122
• Chapter 7. Statistical Mechanics Approximations of BBO 123 7.1. Preliminary foundation 123 7.2. Statistical mechanics model of BBO 128 7.2.1. Migration 128 7.2.2. Mutation 134 7.3. Further discussion 141 7.3.1. Finite population effects 141 7.3.2. Separable fitness functions 142 7.4. Conclusions 143
• Chapter 8. BBO for Combinatorial Optimization 145 8.1. Traveling salesman problem 147 8.2. BBO for the TSP 148 8.2.1. Population initialization 148 8.2.2. Migration in the TSP 150 8.2.3. Mutation in the TSP 157 8.2.4. Implementation framework 159 8.3. Graph coloring 163 8.4. Knapsack problem 165 8.5. Conclusion 167
• Chapter 9. Constrained BBO 169 9.1. Constrained optimization 170 9.2. Constraint-handling methods 172 9.2.1. Static penalty methods 172 9.2.2. Superiority of feasible points 173 9.2.3. The eclectic evolutionary algorithm 174 9.2.4. Dynamic penalty methods 174 9.2.5. Adaptive penalty methods 176 9.2.6. The niched-penalty approach 177 9.2.7. Stochastic ranking 178 9.2.8. -level comparisons 178 9.3. BBO for constrained optimization 179 9.4. Conclusion 185
• Chapter 10. BBO in Noisy Environments 187 10.1. Noisy fitness functions 188 10.2. Influence of noise on BBO 190 10.3. BBO with re-sampling 193 10.4. The Kalman BBO 196 10.5. Experimental results 199 10.6. Conclusion 201
• Chapter 11. Multi-objective BBO 203 11.1. Multi-objective optimization problems 204 11.2. Multi-objective BBO 211 11.2.1. Vector evaluated BBO 211 11.2.2. Non-dominated sorting BBO 213 11.2.3. Niched Pareto BBO 216 11.2.4. Strength Pareto BBO 218 11.3. Real-world applications 223 11.3.1. Warehouse scheduling model 223 11.3.2. Optimization of warehouse scheduling 229 11.4. Conclusion 231
• Chapter 12. Hybrid BBO Algorithms 233 12.1. Opposition-based BBO 234 12.1.1. Opposition definitions and concepts 234 12.1.2. Oppositional BBO 236 12.1.3. Experimental results 238 12.2. BBO with local search 240 12.2.1. Local search methods 240 12.2.2. Simulation results 245 12.3. BBO with other EAs 247 12.3.1. Iteration-level hybridization 247 12.3.2. Algorithm-level hybridization 250 12.3.3. Experimental results 254 12.4. Conclusion 256 Appendices 259 Appendix A. Unconstrained Benchmark Functions 261 Appendix B. Constrained Benchmark Functions 265 Appendix C. Multi-objective Benchmark Functions 289 Bibliography 309 Index 325.
• (source: Nielsen Book Data)

Evolutionary computation algorithms are employed to minimize functions with large number of variables. Biogeography-based optimization (BBO) is an optimization algorithm that is based on the science of biogeography, which researches the migration patterns of species. These migration paradigms provide the main logic behind BBO. Due to the cross-disciplinary nature of the optimization problems, there is a need to develop multiple approaches to tackle them and to study the theoretical reasoning behind their performance. This book explains the mathematical model of BBO algorithm and its variants created to cope with continuous domain problems (with and without constraints) and combinatorial problems.
(source: Nielsen Book Data)

• Chapter 1 Introduction.-
• Part 1 Basic Concepts.-
• Chapter 2 Multi-objective Optimization Problem.-
• Chapter 3 Treatment of multi-objective Optimization Problem.-
• Part 2 Methodology.-
• Chapter 4 Self-Adaptive Multi-objective Optimization Differential Evolution.-
• Part 3 Applications.-
• Chapter 5 Mathematical.-
• Chapter 6 Engineering.-
• Part 4 Final Considerations.-
• Chapter 7 Conclusions.
• (source: Nielsen Book Data)

This book is aimed at undergraduate and graduate students in applied mathematics or computer science, as a tool for solving real-world design problems. The present work covers fundamentals in multi-objective optimization and applications in mathematical and engineering system design using a new optimization strategy, namely the Self-Adaptive Multi-objective Optimization Differential Evolution (SA-MODE) algorithm. This strategy is proposed in order to reduce the number of evaluations of the objective function through dynamic update of canonical Differential Evolution parameters (population size, crossover probability and perturbation rate). The methodology is applied to solve mathematical functions considering test cases from the literature and various engineering systems design, such as cantilevered beam design, biochemical reactor, crystallization process, machine tool spindle design, rotary dryer design, among others.
(source: Nielsen Book Data)

• Wilson Lamb: Applying functional analytic techniques to evolution equations.- Adam Bobrowski: Boundary conditions in evolutionary equations in biology.-Ernesto Estrada: Introduction to Complex Networks: Structure and Dynamics.-Jacek Banasiak: Kinetic models in natural sciences.- Philippe Laurencot: Weak compactness techniques and coagulation equations.- Ryszard Rudnicki: Stochastic operators and semigroups and their applications in physics and biology.- Mustapha Mokhtar-Kharroubi: Spectral theory for neutron transport.-Anna Marciniak-Czochra: Reaction-diffusion-ODE models of pattern formation.- Mapundi Kondwani Banda: Nonlinear Hyperbolic Systems of Conservation Laws and Related Applications.
• (source: Nielsen Book Data)

With the unifying theme of abstract evolutionary equations, both linear and nonlinear, in a complex environment, the book presents a multidisciplinary blend of topics, spanning the fields of theoretical and applied functional analysis, partial differential equations, probability theory and numerical analysis applied to various models coming from theoretical physics, biology, engineering and complexity theory. Truly unique features of the book are: the first simultaneous presentation of two complementary approaches to fragmentation and coagulation problems, by weak compactness methods and by using semigroup techniques, comprehensive exposition of probabilistic methods of analysis of long term dynamics of dynamical systems, semigroup analysis of biological problems and cutting edge pattern formation theory. The book will appeal to postgraduate students and researchers specializing in applications of mathematics to problems arising in natural sciences and engineering.
(source: Nielsen Book Data)

• chapter 1. Introduction
• chapter 2. Methods of optimal search and screening
• chapter 3. Methods of optimal foraging
• chapter 4. Models of individual search and foraging
• chapter 5. Coalitional search and swarm dynamics
• chapter 6. Remarks on swarm robotic systems for search and foraging
• chapter 7. Conclusion

• Introduction. Methods of Optimal Search and Screening. Methods of Optimal Foraging. Models of Individual Search and Foraging. Coalitional Search and Swarm Dynamics. Remarks on Swarm Robotic Systems for Search and Foraging. Conclusion. Bibliography. Index.
• (source: Nielsen Book Data)

• Introduction Group Testing Search and Screening Games of Search Foraging Goal and Structure of This Book
• Methods of Optimal Search and Screening Location Probabilities and Search Density Search for a Static Target Search for a Moving Target
• Methods of Optimal Foraging Preying and Foraging by Patches Spatial Dynamics of Populations Methods of Optimal Foraging Inferences and Restrictions
• Models of Individual Search and Foraging Movements of the Agents and Their Trajectories Brownian Search and Foraging Foraging by Levy Flights Algorithms of Probabilistic Search and Foraging
• Coalitional Search and Swarm Dynamics Swarming and Collective Foraging Foraging by Multiple Foragers in Random Environment Modeling by Active Brownian Motion Turing System for the Swarm Foraging
• Remarks on Swarm Robotic Systems for Search and Foraging
• Conclusion
• Bibliography
• Index
• A Summary appears at the end of each chapter.
• (source: Nielsen Book Data)

Since the start of modern computing, the studies of living organisms have inspired the progress in developing computers and intelligent machines. In particular, the methods of search and foraging are the benchmark problems for robotics and multi-agent systems. The highly developed theory of search and screening involves optimal search plans that ar.
(source: Nielsen Book Data)
Since the start of modern computing, the studies of living organisms have inspired the progress in developing computers and intelligent machines. In particular, the methods of search and foraging are the benchmark problems for robotics and multi-agent systems. The highly developed theory of search and screening involves optimal search plans that are obtained by standard optimization techniques while the foraging theory addresses search plans that mimic the behavior of living foragers. Search and Foraging: Individual Motion and Swarm Dynamics examines how to program artificial search agents so that they demonstrate the same behavior as predicted by the foraging theory for living organisms. For cybernetics, this approach yields techniques that enable the best online search planning in varying environments. For biology, it allows reasonable insights regarding the internal activity of living organisms performing foraging tasks. The book discusses foraging theory as well as search and screening theory in the same mathematical and algorithmic framework. It presents an overview of the main ideas and methods of foraging and search theories, making the concepts of one theory accessible to specialists of the other. The book covers Brownian walks and Levy flight models of individual foraging and corresponding diffusion models and algorithms of search and foraging in random environments both by single and multiple agents. It also describes the active Brownian motion models for swarm dynamics with corresponding Fokker-Planck equations. Numerical examples and laboratory verifications illustrate the application of both theories.
(source: Nielsen Book Data)

• Introduction.- Fundamentals of Search, Optimization and Decision Making.- Multiobjective EA Basics.- Multiobjective Cybernetics - Does Nature Solve Problems and Are They Multiobjective?.- Modularity Causes Multiple Objectives in Natural and Computational Systems.- Problem Decomposition, Modularity and their relation to Multiple Objectives.- Spatial Predator-Prey Models of Multiobjective Optimization.- Solution Concepts in Co-evolution and Multi-objective Search.- How Multiple Objectives are Used and their Effects on Solution Selection Methods.- Ill-Defined Problem Spaces.- Constrained Optimization via MOEAs.- Multiobjectivization.- Helper Objectives.- Learning Evaluation Functions for Global Optimization.- Assessing the Intrinsic Number of Objectives.- Assessing the Intrinsic Number of Decision Variables.- Fuzzy Dominance, Favour and other Relations beyond Pareto Optimality.- Multiobjective Clustering.- Reducing Bloat in GP with Multiple Objectives.- Multiobjective GP for Human-Understandable Models.- Multiobjective Supervised Learning.- Multiobjective Association Rule Mining.- Protein-folding via MOEAs and Solution Selection.- Unveiling Salient Insights in Engineering Designs with MOEAs.- Conclusions.- MOEA: Triumph of Natural Computing.- References.- Index.
• (source: Nielsen Book Data)

Multiobjective problems involve several competing measures of solution quality, and multiobjective evolutionary algorithms (MOEAs) and multiobjective problem solving have become important topics of research in the evolutionary computation community over the past 10 years. This is an advanced text aimed at researchers and practitioners in the area of search and optimization. The book focuses on how MOEAs and related techniques can be used to solve problems, particularly in the disciplines of science and engineering. Contributions by leading researchers show how the concepts of multi objective optimization can be used to reformulate and resolve problems in broad areas such as constrained optimization, co evolution, classification, inverse modeling and design. The book is distinguished from other texts on MOEAs in that it is not primarily about the algorithms, nor specific applications, but about the concepts and processes involved in solving problems using a multi objective approach. Each chapter contributes to the central, deep concepts and themes of the book: evaluating the utility of the multi objective approach; discussing alternative problem formulations; showing how problem formulation affects the search process; and examining solution selection and decision making. The book will be of benefit to researchers, practitioners and graduate students engaged with optimization-based problem solving. For multi objective optimization experts, the book is an up-to-date account of emerging and advanced topics; for others, the book indicates how the multi objective approach can lead to fresh insights.
(source: Nielsen Book Data)

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

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

• An Introduction to Multi-Objective Evolutionary Algorithms and Their Applications
• Applications of Multi-Objective Evolutionary Algorithms in Engineering Design
• Optimal Design of Industrial Electromagnetic Devices: A Multiobjective Evolutionary Approach
• Groundwater Monitoring Design: A Case Study Combining Epsilon Dominance Archiving and Automatic Parameterization for the NSGA-II
• Using a Particle Swarm Optimizer with a Multi-Objective Selection Scheme to Design Combinational Logic Circuits
• Application of Multi-Objective Evolutionary Algorithms in Autonomous Vehicles Navigation
• Automatic Control System Design via a Multiobjective Evolutionary Algorithm
• The Use of Evolutionary Algorithms to Solve Practical Problems in Polymer Extrusion
• The Use of Evolutionary Algorithms to Solve Practical Problems in Polymer Extrusion
• City and Regional Planning via a MOEA: Lessons Learned
• A Multi-Objective Evolutionary Algorithm for the Covering Tour Problem
• A Computer Engineering Benchmark Application for Multiobjective Optimizers
• Multiobjective Aerodynamic Design and Visualization of Supersonic Wings by Using Adaptive Range Multiobjective Genetic Algorithms
• Applications of a Multi-Objective Genetic Algorithm in Chemical and Environmental Engineering
• Multi-Objective Spectroscopic Data Analysis of Inertial Confinement Fusion Implosion Cores: Plasma Gradient Determination
• Application of Multiobjective Evolutionary Optimization Algorithms in Medicine
• On Machine Learning with Multiobjective Genetic Optimization
• Generalized Analysis of Promoters: A Method for DNA Sequence Description
• Multi-Objective Evolutionary Algorithms for Computer Science Applications
• Design of Fluid Power System Using a Multi Objective Genetic Algorithm
• Elimination of Exceptional Elements in Cellular Manufacturing Systems Using Multi-Objective Genetic Algorithms
• Single-Objective and Multi-Objective Evolutionary Flowshop Scheduling
• Evolutionary Operators Based on Elite Solutions for Bi-Objective Combinatorial Optimization
• Multi-Objective Rectangular Packing Problem
• Multi-Objective Algorithms for Attribute Selection in Data Mining
• Financial Applications of Multi-Objective Evolutionary Algorithms: Recent Developments and Future Research Directions
• Evolutionary Multi-Objective Optimization Approach to Constructing Neural Network Ensembles for Regression
• Optimizing Forecast Model Complexity Using Multi-Objective Evolutionary Algorithms
• Even Flow Scheduling Problems in Forest Management
• Using Diversity to Guide the Search in Multi-Objective Optimization.

• Intro; Preface; Organization; Contents; Theory; On Bi-objective Convex-Quadratic Problems; 1 Introduction; 2 Theoretical Properties of Bi-objective Convex-Quadratic Problems; 2.1 Preliminaries; 2.2 Pareto Set; 2.3 Convexity of the Pareto Front; 3 New Classes of Bi-objective Test Functions; 4 Summary; References; An Empirical Investigation of the Optimality and Monotonicity Properties of Multiobjective Archiving Methods; 1 Introduction; 2 Experimental Design; 2.1 Assessment Indexes; 2.2 Archivers Investigated; 2.3 Test Problems; 2.4 General Experimental Settings; 3 Results; 3.1 Optimal Ratio

• 3.2 Deterioration Ratio3.3 Summary; 4 Concluding Remarks; References; Evolutionary Multi-objective Optimization Using Benson's Karush-Kuhn-Tucker Proximity Measure; 1 Introduction; 2 KKT Based Proximity Measure; 3 Proposed B-KKT Proximity Measure; 4 Results; 4.1 Two-Objective Optimization Problems; 4.2 Three-Objective Optimization Problems; 4.3 Many-Objective Optimization Problems; 4.4 Engineering Design Problem; 5 Conclusions; References; On the Convergence of Decomposition Algorithms in Many-Objective Problems; 1 Introduction; 2 Numerical Experiments; 3 Interpretation of Results

• 4 ConclusionReferences; Algorithms; A New Hybrid Metaheuristic for Equality Constrained Bi-objective Optimization Problems; 1 Introduction; 2 Background; 3 Proposed Algorithm (M-NSGA-II/PT); 3.1 First Stage: Rough Approximation via Micro-NSGA-II; 3.2 Second Stage: Refinement via PT; 4 Numerical Results; 5 Conclusions and Future Work; References; Make Evolutionary Multiobjective Algorithms Scale Better with Advanced Data Structures: Van Emde Boas Tree for Non-dominated Sorting; 1 Introduction; 2 Preliminaries; 3 The Divide-and-Conquer Algorithm for Non-dominated Sorting; 3.1 The General Plan

• 3.2 Sweep Line Algorithms for m = 24 The Van Emde Boas Tree; 5 Efficient Implementation of the Van Emde Boas Tree; 6 Implementation and Analysis of the Whole Algorithm; 7 Experiments; 8 Conclusion; References; Toward a New Family of Hybrid Evolutionary Algorithms; 1 Introduction; 2 Background; 3 Subspace Polynomial Mutation Operator; 4 Multi-objective Descent Directions Within MOEAs; 4.1 Equality Constrained MOPs; 4.2 Gradient-Free Descent Direction; 5 Application: Hybrid Algorithm for Constrained Optimization; 6 Conclusions and Future Work; References

• Adjustment of Weight Vectors of Penalty-Based Boundary Intersection Method in MOEA/D1 Introduction; 2 Related Works; 3 MOEA/D-PBI with Adjusted Weight Vectors; 4 Computational Experiments; 4.1 Experimental Settings; 4.2 Experimental Results; 5 Conclusions; References; GD
• E4: The Generalized Differential Evolution with Ordered Mutation; 1 Introduction; 2 Background Review; 2.1 Generalized Differential Evolution; 2.2 Existing Single Objective Differential Evolution with Ordered Mutation; 3 Proposed Algorithm: The Generalized Differential Evolution with the Ordered Mutation (GDE4); 4 Experiment

This book constitutes the refereed proceedings of the 10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019 held in East Lansing, MI, USA, in March 2019. The 59 revised full papers were carefully reviewed and selected from 76 submissions. The papers are divided into 8 categories, each representing a key area of current interest in the EMO field today. They include theoretical developments, algorithmic developments, issues in many-objective optimization, performance metrics, knowledge extraction and surrogate-based EMO, multi-objective combinatorial problem solving, MCDM and interactive EMO methods, and applications.
(source: Nielsen Book Data)

• Plenary Talks
• Interactive Approaches in Multiple Criteria Decision Making and Evolutionary Multi-objective Optimization
• Towards Automatically Configured Multi-objective Optimizers
• A Review of Evolutionary Multiobjective Optimization Applications in Aerospace Engineering
• Performance evaluation of multiobjective optimization algorithms: quality indicators and the attainment function
• Theory and Hyper-Heuristics
• A Multimodal Approach for Evolutionary Multi-objective Optimization (MEMO): Proof-of-Principle Results
• Unwanted Feature Interactions Between the Problem and Search Operators in Evolutionary Multi-objective Optimization
• Neutral but a Winner! How Neutrality helps Multiobjective Local Search Algorithms
• To DE or not to DE? Multi-Objective Differential Evolution Revisited from a Component-Wise Perspective
• Model-Based Multi-Objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmark
• Temporal Innovization: Evolution of Design Principles Using Multi-objective Optimization
• MOEA/D-HH: A Hyper-Heuristic for Multi-objective Problems
• Using hyper-heuristic to select leader and archiving methods for many-objective problems
• Algorithms
• Adaptive Reference Vector Generation for Inverse Model Based Evolutionary Multiobjective Optimization with Degenerate and Disconnected Pareto Fronts
• MOEA/PC: Multiobjective Evolutionary Algorithm Based on Polar Coordinates
• GD-MOEA: A New Multi-Objective Evolutionary Algorithm based on the Generational Distance Indicator
• Experiments on Local Search for Bi-objective Unconstrained Binary Quadratic Programming
• A Bug in the Multiobjective Optimizer IBEA: Salutary Lessons for Code Release and a Performance Re-Assessment
• A Knee-based EMO Algorithm with an Efficient Method to Update Mobile Reference Points
• A Hybrid Algorithm for Stochastic Multiobjective Programming Problem
• Parameter Tuning of MOEAs using a Bilevel Optimization Approach
• Pareto adaptive scalarising functions for decomposition based algorithms
• A bi-level multiobjective PSO algorithm
• An interactive simple indicator-based evolutionary algorithm (I-SIBEA) for multiobjective optimization problems
• Combining Non-dominance, Objective-sorted and Spread Metric to Extend Firefly Algorithm to Multi-objective Optimization
• GACO: a parallel evolutionary approach to multi-objective scheduling
• Kriging Surrogate Model Enhanced by Coordinate Transformation of Design Space Based on Eigenvalue Decomposition
• A Parallel Multi-Start NSGA II Algorithm for Multiobjective Energy Reduction Vehicle Routing Problem
• Evolutionary Inference of Attribute-based Access Control Policies
• Hybrid Dynamic Resampling for Guided Evolutionary Multi-Objective Optimization
• A Comparison of Decoding Strategies for the 0/1 Multi-objective Unit Commitment Problem
• Comparing Decomposition-based and Automatically Component-Wise Designed Multi-objective Evolutionary Algorithms
• Upper Confidence Bound (UCB) Algorithms for Adaptive Operator Selection in MOEA/D
• Towards Understanding Bilevel Multi-objective Optimization with Deterministic Lower Level Decisions.

This book constitutes the refereed proceedings of the 8th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2015 held in Guimarães, Portugal in March/April 2015. The 68 revised full papers presented together with 4 plenary talks were carefully reviewed and selected from 90 submissions. The EMO 2015 aims to continue these type of developments, being the papers presented focused in: theoretical aspects, algorithms development, many-objectives optimization, robustness and optimization under uncertainty, performance indicators, multiple criteria decision making and real-world applications.

• Many-Objectives Optimization, Performance and Robustness
• Evolutionary Many-objective Optimization based on Kuhn-Munkres? Algorithm
• A KKT Proximity Measure for Evolutionary Multi-Objective and Many-Objective Optimization
• U-NSGA-III: A Unified Evolutionary Optimization Procedure for Single, Multiple, and Many Objectives? Proof-of- Principle Results
• Clustering based parallel Many-objective Evolutionary Algorithms using the shape of the objective vectors
• Faster Exact Algorithms for Computing Expected Hypervolume Improvement
• A GPU-based Algorithm for a Faster Hypervolume Contribution Computation
• A Feature-based Performance Analysis in Evolutionary Multiobjective Optimization
• Modified Distance Calculation in Generational Distance and Inverted Generational Distance
• On the Behavior of Stochastic Local Search within Parameter Dependent MOPs
• An Evolutionary Approach to Active Robust Multiobjective Optimisation
• Linear scalarization Pareto front identification in stochastic environments
• Elite Accumulative Sampling Strategies for Noisy Multi-Objective Optimisation
• Guideline Identification for Optimization under Uncertainty through the Optimization of a Boomerang Trajectory
• MCDM
• Using indifference information in robust ordinal regression
• A Multi-objective genetic algorithm for inferring inter-criteria parameters for water supply consensus
• Genetic Algorithm Approach for a Class of Multi-criteria, Multi-vehicle Planner of UAVs
• An Interactive Evolutionary Multiobjective Optimization Method: Interactive WASF-GA
• On Generalizing Lipschitz Global Methods for Multiobjective Optimization
• Dealing with scarce optimization time in complex logistics optimization: A study on the biobjective Swap-Body Inventory Routing Problem
• Machine Decision Makers as a Laboratory for Interactive EMO
• Real World Applications
• Aircraft Air Inlet Design Optimization via Surrogate-Assisted Evolutionary Computation
• Diesel Engine Drive-Cycle Optimization with the Integrated Optimization Environment? Liger
• Re-design for robustness: An approach based on many objective optimization
• A Model for a Human Decision-Maker in a Polymer Extrusion Process
• Multi-Objective Optimization of Gate Location and Processing Conditions in Injection Molding Using MOEAs: Experimental Assessment
• A Multi-Criteria Decision Support System for a Routing Problem in Waste Collection
• Application of Evolutionary Multiobjective Algorithms for solving the problem of Energy Dispatch in Hydroelectric Power Plants
• Solutions in Under 10 Seconds for Vehicle Routing Problems with Time Windows using Commodity Computers
• A comparative study of algorithms for solving the Multiobjective Open-Pit Mining Operational Planning Problems
• A Model to Select a Portfolio of Multiple Spare Parts for a Public Bus Transport Service Using NSGA II.-A Multi-Objective Optimization Approach Associated to Climate Change Analysis to Improve Systematic Conservation Planning
• Marginalization in Mexico: An Application of the Electre III[Pleaseinsertintopreamble]MOEA Methodology
• Integrating Hierarchical Clustering and Pareto-Efficacy to Preventive Controls Selection in Voltage Stability Assessment
• Multi-objective Evolutionary Algorithm with Discrete Differential Mutation Operator for Service Restoration in Largescale
• Distribution Systems
• Combining Data Mining and Evolutionary Computation for Multi-Criteria Optimization of Earthworks
• Exploration of Two-Objective Scenarios on Supervised Evolutionary Feature Selection: a Survey and a Case Study
• (Application to Music Categorisation)
• A Multi-Objective Approach for Building Hyperspectral Remote Sensed Image Classifier Combiners
• Multi-Objective Optimization of Barrier Coverage with Wireless Sensors
• Comparison of Single and Multi-objective Evolutionary Algorithms for Robust Link-state Routing.

This book constitutes the refereed proceedings of the 8th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2015 held in Guimarães, Portugal in March/April 2015. The 68 revised full papers presented together with 4 plenary talks were carefully reviewed and selected from 90 submissions. The EMO 2015 aims to continue these type of developments, being the papers presented focused in: theoretical aspects, algorithms development, many-objectives optimization, robustness and optimization under uncertainty, performance indicators, multiple criteria decision making and real-world applications.

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

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

This book constitutes the refereed proceedings for the 4th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2004, held in Coimbra, Portugal, in April together with EuroGP 2004 and six workshops on evolutionary computing. The 23 revised full papers presented were carefully reviewed and selected from 86 submissions. Among the topics addressed are evolutionary algorithms as well as metaheuristics like memetic algorithms, ant colony optimization, and scatter search; the papers are dealing with representations, operators, search spaces, adaptation, comparison of algorithms, hybridization of different methods, and theory. Among the combinatorial optimization problems studied are graph coloring, network design, cutting, packing, scheduling, timetabling, traveling salesman, vehicle routing, and various other real-world applications.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of three workshops on the application of evolutionary programming and algorithms in various domains; these workshops were held in conjunction with the 5th European Conference on Genetic Programming, EuroGP 2002, in Kinsale, Ireland, in April 2002. The 33 revised full papers presented were carefully reviewed and selected by the respective program committees. In accordance with the three workshops EvoCOP, EvoIASP, and EvoSTIM/EvoPLAN, the papers are organized in topical sections on combinatorial optimization problems; image analysis and signal processing; and scheduling, timetabling, and AI planning.
(source: Nielsen Book Data)

• An overview of evolutionary algorithms and their advantages
• evolving models of time series
• evolutionary clustering and classification
• evolving control systems
• theory and tools for improving evolutionary algorithms.
• (source: Nielsen Book Data)

Evolutionary cmputation is one of the fastest growing areas of computer science, partly because of its broad applicability to engineering problems. The methods can be applied to problems as diverse as supply-chain optimization, routing and planning, task assignment, pharmaceutical design, interactive gaming, and many others within the signal processing domain. The text is an outgrowth of a series of SPIE short courses taught by the author. The examples span a range of applications and should be useful to a variety of readers of mixed backgrounds and expertise.
(source: Nielsen Book Data)

Annotation Genetic Algorithms Genetic Programming Evolution Strategies Evolutionary Programming Differential Evolution Artificial Immune Systems Particle Swarm Optimization Ant Colony Optimization Bacterial Foraging Artificial Bees Harmony Search Gravitational search algorithm Quantum Computing Memetic Computing Fireflies Algorithm Hybridization of Algorithms Imperialism Competitive Learning Tabu Search Simulated Annealing Bat Algorithm intelligent water drop Other Metaheuristics.

• Adaptation, Self-Adaptation and Parameter Tuning
• Classifier Systems, Differential Evolution and Swarm Intelligence
• Coevolution and Artificial Immune Systems
• Constraint Handling
• Dynamic and Uncertain Environments
• Estimation of Distribution Algorithms and Metamodelling
• Genetic Programming
• Multi-objective Optimisation
• Parallel Algorithms and Hardware Implementations
• Real-World Applications
• Theory.

This book constitutes the refereed proceedings of the 13th International Conference on Parallel Problem Solving from Nature, PPSN 2013, held in Ljubljana, Slovenia, in September 2014. The total of 90 revised full papers were carefully reviewed and selected from 217 submissions. The meeting began with 7 workshops which offered an ideal opportunity to explore specific topics in evolutionary computation, bio-inspired computing and metaheuristics. PPSN XIII also included 9 tutorials. The papers are organized in topical sections on adaption, self-adaption and parameter tuning; classifier system, differential evolution and swarm intelligence; coevolution and artificial immune systems; constraint handling; dynamic and uncertain environments; estimation of distribution algorithms and metamodelling; genetic programming; multi-objective optimisation; parallel algorithms and hardware implementations; real world applications; and theory.

• Computational brittleness and the evolution of computer viruses
• Evolutionary computing in multi-agent environments: Speciation and symbiogenesis
• Evolution strategies with subjective selection
• Emergent cooperation for multiple agents using genetic programming
• Evolution programs evolved
• Encoding scheme issues for open-ended artificial evolution
• Hardware evolution at function level
• Coevolutionary life-time learning
• Genetic programs and co-evolution
• Self-assemblage of gene nets in evolution via recruiting of new netters
• A survey of intron research in genetics
• Analytical and numerical investigations of evolutionary algorithms in continuous spaces
• On the asymptotic behavior of multirecombinant Evolution Strategies
• Are long path problems hard for genetic algorithms?
• Random tree generation for genetic programming
• Implicit formae in genetic algorithms
• A probabilistic database approach to the analysis of genetic algorithms
• Mean field analysis of tournament selection on a random manifold
• From recombination of genes to the estimation of distributions I. Binary parameters
• From recombination of genes to the estimation of distributions II. Continuous parameters
• Searching in the presence of noise
• The density of states
• A measure of the difficulty of optimisation problems
• On interactive evolutionary algorithms and stochastic mealy automata
• The influence of different coding schemes on the computational complexity of genetic algorithms in function optimization
• An analysis of the effects of neighborhood size and shape on local selection algorithms
• Evolutionary computation at the edge of feasibility
• Dimensional analysis of allele-wise mixing revisited
• Gaussian diffusion in a simple genetic algorithm
• Erroneous truncation selection
• A breeder's decision making perspective
• New crossover methods for sequencing problems
• The effect of extensive use of the mutation operator on generalization in genetic programming using sparse data sets
• On permutation representations for scheduling problems
• Multi-parent's niche: N-ary crossovers on NK-landscapes
• A preliminary investigation into directed mutations in evolutionary algorithms
• Heuristic crossovers for real-coded genetic algorithms based on fuzzy connectives
• Are evolutionary algorithms improved by large mutations?
• Mutation by imitation in boolean evolution strategies
• Formal algorithms + formal representations =search strategies
• A genetic algorithm with variable range of local search for tracking changing environments
• An Evolution Strategy with adaptation of the step sizes by a variance function
• Every niching method has its niche: Fitness sharing and implicit sharing compared
• Effects of isolation in a distributed population genetic algorithm
• Self-adaptive genetic algorithm for numeric functions
• Niche search: An evolutionary algorithm for global optimisation
• Adaptively parameterised evolutionary systems: Self adaptive recombination and mutation in a genetic algorithm
• Obtaining multiple distinct solutions with genetic algorithm niching methods
• Cost Based Operator Rate Adaptation: An investigation
• Genetic algorithms and relational landscapes
• IOGA: An instance-oriented genetic algorithm
• Explicit filtering of building blocks for genetic algorithms
• Multi-objective optimization by means of the thermodynamical genetic algorithm
• Adaptation to a changing environment by means of the thermodynamical genetic algorithm
• The development of a dual-agent strategy for efficient search across whole system engineering design hierarchies
• A parallel cellular genetic algorithm used in finite element simulation
• A robust solution searching scheme in genetic search
• Solving MasterMind using GAs and simulated annealing: A case of dynamic constraint optimization
• Evolving compact solutions in genetic programming: A case study
• Climbing up NP-hard hills
• On the performance assessment and comparison of stochastic multiobjective optimizers
• Paginating the generalized newspapers
• A comparison of simulated annealing and a heuristic method
• A comparison of optimization techniques for integrated manufacturing planning and scheduling
• A comparison of search techniques on a wing-box optimisation problem
• A comparative study of evolutionary algorithms for on-line parameter tracking
• Modeling urban growth by cellular automata
• Democratic optimization for discrete and continuous systems
• A study of some properties of Ant-Q
• Immunoid: An immunological approach to decentralized behavior arbitration of autonomous mobile robots
• Parallelizable evolutionary dynamics principles for solving the maximum clique problem
• Significance of locality and selection pressure in the grand deluge evolutionary algorithm
• Parallel computing with DNA: Toward the anti-universal machine
• Tackling the "curse of dimensionality" of radial basis functional neural networks using a genetic algorithm
• A Three-stage method for designing Genetic Fuzzy Systems by learning from examples
• Learning heuristics for OBDD minimization by Evolutionary Algorithms
• Improving the generalization performance of multi-layer-perceptrons with population-based incremental learning
• Robust GP in robot learning
• A pattern recognition system using evolvable hardware
• Topology design of feedforward neural networks by genetic algorithms
• An evolution strategy for on-line optimisation of dynamic objective functions
• Exploiting competing subpopulations for automatic generation of test sequences for digital circuits
• Constraint handling in evolutionary search: A case study of the frequency assignment
• An application of genetic algorithms and neural networks to scheduling power generating systems
• Evolutionary algorithms for the calculation of electron distributions in Si-MOSFETs
• Refueling of a nuclear power plant: Comparison of a naive and a specialized mutation operator
• Genetic algorithms applied to the physical design of VLSI circuits: A survey
• Stochastic methods for transistor size optimization of CMOS VLSI circuits
• An adaptive parallel Genetic Algorithm for VLSI-layout optimization
• Genetic algorithms for protocol validation
• Constraint handling for the fault coverage code generation problem: An inductive evolutionary approach
• New genetic local search operators for the traveling salesman problem
• An evolutionary approach to hardware/software partitioning
• Evolutionary Air Traffic Flow Management for large 3D-problems
• Genetic-based dynamic load balancing: Implementation and evaluation
• Production scheduling with genetic algorithms and simulation
• Network optimization using evolutionary strategies
• Co-evolving parallel random number generators
• Scheduling by genetic local search with multi-step crossover
• Finding the conformation of organic molecules with genetic algorithms
• Investigating a Parallel Breeder Genetic Algorithm on the inverse Aerodynamic design
• An evolutionary design for f-? lenses
• Optimization of heat exchanger networks by means of evolution strategies
• Industrial plant pipe-route optimisation with genetic algorithms
• An evolutionary algorithm for design optimization of microsystems
• A learning classifier system for three-dimensional shape optimization.

This book constitutes the refereed proceedings of the International Conference on Evolutionary Computation held jointly with the 4th Conference on Parallel Problem Solving from Nature, PPSN IV, in Berlin, Germany, in September 1996. The 103 revised papers presented in the volume were carefully selected from more than 160 submissions. The papers are organized in sections on basic concepts of evolutionary computation (EC), theoretical foundations of EC, modifications and extensions of evolutionary algorithms, comparison of methods, other metaphors, and applications of EC in a variety of areas like ML, NNs, engineering, CS, OR, and biology. The book has a comprehensive subject index.

• Preface. Committee and Sponsors. Evolutionary computation in crack problems
• W. Beluch. Investigation of evolutionary algorithm effectiveness in optimal synthesis of certain mechanisms
• K. Bialas-Hetltowski, et al. Minimum heat losses subjected to stiffness constraints: window frame optimization
• R.A. Bialecki, M. Krol. Evolutionary computation in inverse problems
• T. Burczynski, et al. Hang-glider wing design by genetic optimization
• S. D'Angelo, et al. An error function for optimum dimension synthesis of mechanisms using genetic algorithms
• I.F. de Bustos, et al. Evolutionary computation in thermoelastic problems
• A. Dlugosz. Management of evolutionary MAS for multiobjective optimisation
• G. Dobrowalski, M. Kisiel-Dorohinicki. PAMUC: a new method to handle with constraints and multiobjectivity in evolutionary algorithms
• R. Filomeno Coelho, et al. A comparative analysis of "controlled elitism" in the NGSA-II applied to frame optimization
• D. Greiner, et al. IS-PAES: multiobjective optimization with efficient constraint handling
• A. Hernandez Aguirre, et al. Optimization of aligned fiber laminate composites
• Z. Hu, et al. Genetic algorithm for damage assessment
• V.T. Johnson, et al. Estimation of parameters for a hydrodynamic transmission system mathematical model with the application of genetic algorithm
• A. Kesy, et al. Study of safety of high-rise buildings using evolutionary search
• S. Khajhpour, D.E. Grierson. Structural design using genetic algorithm
• E. Kita, et al. The topology optimization using evolutionary search
• G. Kokot, P. Orantek. Identification of CMM parametric errors by hierarchical genetic strategy
• J. Kolodziej, et al. Genetic algorithm for fatigue crack detection in Timoshenko beam
• M. Krawczuk, et al. Multicriteria design optimization of robot gripper mechanisms
• S. Krenich. Optimal design of multiple clutch brakes using a multistage evolutionary method
• S. Krenich, A. Osyczka. Distributed evolutionary algorithms in optimization of nonlinear solids
• W. Kus, T. Burczynski. Adaptive penalty strategies in genetic search for problems with inequality and equality constraints
• C.-Y. Lin, W.-H. Wu. On the identification of linear elastic mechanical behaviour of orthopedic materials using evolutionary algorithms
• M. Magalhaes Dourado, et al. Ranking pareto optimal solutions in genetic algorithm by using undifferentiation interval method
• J. Montusiewicz. The effectiveness of probabilistic algorithms in shape and topology discrete optimisation of 2-D composite structures
• A. Muc. Genetic algorithms in optimisation of resin hardening technological processes
• A. Muc, P. Saj. Hybrid evolutionary algorithms in optimization of structures under dynamical loads
• P. Orantek. Evolutionary optimization system (EOS) for design automation
• O. Osyczka, et al. Evolutionary method for a universal motor geometry optimization
• G. Papa, B. Korousic-Seljak. A review of the development and application of cluster oriented genetic algorithms
• I.C. Parmee. Genetic algorithm optimization of hole shapes in a perforated elastic plate over a range of loads
• S. Vigdergauz. An object oriented library for evolutionary programs with applications in partitioning of finite element meshes
• J. Zola, et al.
• (source: Nielsen Book Data)

This book contains papers presented at the IUTAM Symposium on Evolutionary Methods in Mechanics held in Cracow, Poland, September 24-27, 2002. Recently, Evolutionary algorithms and methods have become the most effective tools for solving specific kinds of problems in mechanics, especially in structural and multidisciplinary optimization. The book is devoted to both theoretical and practical developments of computational mechanics methods drawing their inspiration from nature with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming and other evolutionary computation techniques in mechanics. The special emphasis is put on evolutionary optimization in various fields of mechanics. The subject of evolutionary optimization has recently experienced a remarkable growth. New concepts, approaches and applications are being continually and exploited to provide efficient tools for solving a variety of optimization problems in mechanics. Several important topics are presented: evolutionary methods in shape and topology optimization, evolutionary methods in size and material optimization, evolutionary methods in multiobjective optimization, in inverse problems of engineering mechanics, in biomechanics, coupling of evolutionary methods and neural networks in mechanics, methods of artificial intelligence in mechanics based on evolutionary approaches and other unconventional applications in mechanics. Research workers, specialists and professionals who deal with methods of artificial intelligence, especially in the field of evolutionary computation, structural and multidisciplinary optimization would be interested in this book.
(source: Nielsen Book Data)

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

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

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

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

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

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

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

This book constitutes the refereed proceedings of the 11th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2011, held in Torino, Italy, in April 2011. The 22 revised full papers presented were carefully reviewed and selected from 42 submissions. The papers present the latest research and discuss current developments and applications in metaheuristics - a paradigm to effectively solve difficult combinatorial optimization problems appearing in various industrial, economical, and scientific domains. Prominent examples of metaheuristics are evolutionary algorithms, simulated annealing, tabu search, scatter search, memetic algorithms, variable neighborhood search, iterated local search, greedy randomized adaptive search procedures, estimation of distribution algorithms, and ant colony optimization.
(source: Nielsen Book Data)

• Machine generated contents note:
• Automated Innovization for Simultaneous Discovery of Multiple Rules in Bi-objective Problems
• Taxonomy of Online Stopping Criteria for Multi-Objective Evolutionary Algorithms / Sunith Bandaru / Kalyanmoy Deb
• Not All Parents Are Equal for MO-CMA-ES / Tobias Wagner / Heike Trautmann / Luis Marti
• On Sequential Online Archiving of Objective Vectors / Ilya Loshchilov / Marc Schoenauer / Michèle Sebag
• On a Stochastic Differential Equation Approach for Multiobjective Optimization up to Pareto-Criticality / Manuel López-Ibáñez / Joshua Knowles / Marco Laumanns
• Pareto Cone &Dominance: Improving Convergence and Diversity in Multiobjective Evolutionary Algorithms / Ricardo H.C. Takahashi / Eduardo G. Carrano / Elizabeth F. Wanner
• Variable Preference Modeling Using Multi-Objective Evolutionary Algorithms / Lucas S. Batista / Felipe Campelo / Frederico G. Guimarães /

• Note continued:
• Framework for Many-Objective Test Problems with Both Simple and Complicated Pareto-Set Shapes / Joseph M. Pasia / Hernán Aguirre / Kiyoshi Tanaka
• Preference Based Interactive Evolutionary Algorithm for Multi-objective Optimization: PIE / Dhish Kumar Saxena / Qingfu Zhang / João A. Duro / Ashutosh Tiwari
• Preference Ranking Schemes in Multi-Objective Evolutionary Algorithms / Karthik Sindhya / Ana Belen Ruiz / Kaisa Miettinen
• Interactive Multiobjective Mixed-Integer Optimization Using Dominance-Based Rough Set Approach / Marlon Alexander Braun / Pradyumn Kumar Shukla / Hartmut Schmeck
• Very Large-Scale Neighborhood Search for Solving Multiobjective Combinatorial Optimization Problems / Salvatore Greco / Benedetto Matainzzo / Roman Slowinski
• Bilevel Multi-objective Optimization Problem Solving Using Progressively Interactive EMO / Thibaut Lust / Jacques Teghem / Daniel Tuyttens
• Multi-objective Phylogenetic Algorithm: Solving Multi-objective Decomposable Deceptive Problems / Ankur Sinha
• Multi-objective Optimization with Joint Probabilistic Modeling of Objectives and Variables / Jean Paulo Martins / Antonio Helson Mineiro Soares / Danilo Vasconcellos Vargas / Alexandre Cláudio Botazzo Delbem
• Bi-objective Based Hybrid Evolutionary-Classical Algorithm for Handling Equality Constraints / Hossein Karshenas / Roberto Santana / Concha Bielza / Pedro Larrañaga
• New Memory Based Variable-Length Encoding Genetic Algorithm for Multiobjective Optimization / Rituparna Datta / Kalyanmoy Deb
• Concentration-Based Artificial Immune Network for Multi-objective Optimization / Eduardo G. Carrano / Livia A. Moreira / Ricardo H.C. Takahashi
• Bi-objective Portfolio Optimization Using a Customized Hybrid NSGA-II Procedure / Guilherme Palermo Coelho / Fernando J. Von Zuben

• Note continued:
• Aesthetic Design Using Multi-Objective Evolutionary Algorithms / Rahul Tewari / Rajat Tewari / Ralph E. Steuer / Kalyanmoy Deb
• Introducing Reference Point Using g-Dominance in Optimum Design Consideripg Uncertainties: An Application in Structural Engineering / Ferrie van Hattum / Dirk Loyens / António Gaspar-Cunha
• Multiobjective Dynamic Optimization of Vaccination Campaigns Using Convex Quadratic Approximation Local Search / José M. Emperador / Máximo Méndez / Gabriel Winter / David Greiner / Blas Galván
• Adaptive Technique to Solve Multi-objective Feeder Reconfiguration Problem in Real Time Context / Ricardo H.C. Takahashi / Rodrigo T.N. Cardoso / André R. da Cruz
• Variable Neighborhood Multiobjective Genetic Algorithm for the Optimization of Routes on IP Networks / Joao Antonio de Vasconcelos / Walmir Matos Caminhas / Carlos Henrique N. de Resende Barbosa
• Real-Time Estimation of Optical Flow Based on Optimized Haar Wavelet Features / Renata E. Onety / Ricardo H.C. Takahashi / Oriane M. Neto / Gladston J.P. Moreira
• Multi-objective Genetic Algorithm Evaluation in Feature Selection / Jan Salmen / Lukas Caup / Christian Igel
• Cultural Algorithm Applied in a Bi-Objective Uncapacitated Facility Location Problem / Ana Carolina Lorena / Huei Diana Lee / Newton Spolaôr
• Bi-objective Iterated Local Search Heuristic with Path-Relinking for the p-Median Problem / Ricardo Soto / Claudio Cubillos / Guillermo Cabrera / Boris Fernández / Daniela Diaz / José Miguel Rubio
• Framework for Locating Logistic Facilities with Multi-Criteria Decision Analysis / Wellington G. Ribeiro / André G. Santos / Paula M. dos Santos / José E.C. Arroyo
• Lorenz versus Pareto Dominance in a Single Machine Scheduling Problem with Rejection / Gilberto Montibeller / Hugo Yoshizaki

• Note continued:
• GRACE: A Generational Randomized ACO for the Multi-objective Shortest Path Problem / Lionel Amodeo / Farouk Yalaoui / Atefeh Moghaddam
• Modeling Decision-Maker Preferences through Utility Function Level Sets / Marco C. Goldbaiq / Luciana S. Buriol / Leonardo C.T. Bezerra / Elizabeth F.G. Goldbarg
• MCDM Model for Urban Water Conservation Strategies Adapting Simos Procedure for Evaluating Alternatives Intra-criteria / Luciana R. Pedro / Ricardo H.C. Takahashi
• Multicriteria Decision Model for a Combined Burn-In and Replacement Policy / Marcele Elisa Fontana / Danielle Costa Morais / Adiel Teixeira de Almeida
• Applying a Multicriteria Decision Model So as to Analyse the Consequences of Failures Observed in RCM Methodology / Cristiano Alexandre Virginio Cavalcante
• Supplier Selection Based on the PROMETHEE VI Multicriteria Method / Marcelo Hazin Alencar / Adiel Teixeira Almeida.

This book constitutes the refereed proceedings of the 6th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2011, held in Ouro Preto, Brazil, in April 2011. The 42 revised full papers presented were carefully reviewed and selected from 83 submissions. The papers deal with fundamental questions of EMO theory, such as the development of algorithmically efficient tools for the evaluation of solution-set quality, the theoretical questions related to solution archiving and others. They report on the continuing effort in the development of algorithms, either for dealing with particular classes of problems or for new forms of processing the problem information. Almost one third of the papers is related to EMO applications in a diversity of fields. Eleven papers are devoted to promote the interaction with the related field of Multi-Criterion Decision Making (MCDM).
(source: Nielsen Book Data)

• Set Oriented Numerics
• Computational Game Theory
• Machine Learning Applied to Networks
• Complex Networks and Landscape Analysis
• Local Search and Optimization
• Genetic Programming
• Evolutionary Multiobjective Optimization
• Practical Aspects of Evolutionary Algorithms.

This volume encloses research articles that were presented at the EVOLVE 2014 International Conference in Beijing, China, July 1-4, 2014. The book gathers contributions that emerged from the conference tracks, ranging from probability to set oriented numerics and evolutionary computation; all complemented by the bridging purpose of the conference, e.g. Complex Networks and Landscape Analysis, or by the more application oriented perspective. The novelty of the volume, when considering the EVOLVE series, comes from targeting also the practitioner's view. This is supported by the Machine Learning Applied to Networks and Practical Aspects of Evolutionary Algorithms tracks, providing surveys on new application areas, as in the networking area and useful insights in the development of evolutionary techniques, from a practitioner's perspective. Complementary to these directions, the conference tracks supporting the volume, follow on the individual advancements of the subareas constituting the scope of the conference, through the Computational Game Theory, Local Search and Optimization, Genetic Programming, Evolutionary Multi-objective optimization tracks.

• EvoCOMNET contributions
• EvoFIN contributions
• EvoIASP contributions -- EvoINTERACTION contributions
• EvoMUSART contributions
• EvoSTOC contributions
• EvoTRANSLOG contributions.

This book constitutes the refereed joint proceedings of seven workshops on evolutionary computing, EvoWorkshops 2007, held in Valencia, Spain in April 2007. It examines evolutionary computation in communications, networks, and connected systems; finance and economics; image analysis and signal processing; and transportation and logistics. Coverage also details evolutionary algorithms in stochastic and dynamic environments.
(source: Nielsen Book Data)

• 1. A cooperative optimization approach for distributing service points in mobility applications / Thomas Jatschka, Tobias Rodemann, and Günther R. Raidl
• 2. A binary algebraic differential evolution for the multidimensional two-way number partitioning problem / Valentino Santucci, Marco Baioletti, Gabriele Di Bari, and Alfredo Milani
• 3. A new representation in genetic programming for evolving dispatching rules for dynamic flexible job shop scheduling / Fangfang Zhang, Yi Mei, and Mengjie Zhang
• 4. An iterated local search algorithm for the two-machine flow shop problem with buffers and constant processing times on one machine / Hoang Thanh Le, Philine Geser, and Martin Middendorf
• 5. Route planning for a fleet of electric vehicles with waiting times at charging stations / Baoxiang Li, Shashi Shekhar Jha, and Hoong Chuin Lau
• 6. Multiple periods vehicle routing problems : a case study / Bilal Messaoudi, Ammar Oulamara, and Nastaran Rahmani
• 7. Rigorous performance analysis of state-of-the-art TSP heuristic solvers / Paul McMenemy, Nadarajen Veerapen, Jason Adair, and Gabriela Ochoa
• 8. Runtime analysis of discrete particle swarm optimization applied to shortest paths computation / Alexander Raß, Jonas Schreiner, and Rolf Wanka
• 9. Quasi-optimal recombination operator / Francisco Chicano, Gabriela Ochoa, Darrell Whitley, and Renato Tinós
• 10. Insights into the feature selection problem using local Optima networks / Werner Mostert, Katherine M. Malan, Gabriela Ochoa, and Andries P. Engelbrecht

• 11. Clarifying the difference in local Optima network sampling algorithms / Sarah L. Thomson, Gabriela Ochoa, and Sébastien Verel
• 12. A unifying view on recombination spaces and abstract convex evolutionary search / Marcos Diez García and Alberto Moraglio
• 13. Program trace optimization with constructive heuristics for combinatorial problems / James McDermott and Alberto Moraglio
• C1. Correction to: Multiple periods vehicle routing problems : a case study / Bilal Messaoudi, Ammar Oulamara, and Nastaran Rahmani.

This book constitutes the refereed proceedings of the 19th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2019, held as part of Evo* 2019, in Leipzig, Germany, in April 2019, co-located with the Evo* 2019 events EuroGP, EvoMUSART and EvoApplications. The 14 revised full papers presented 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 both single- and multi-objective combinatorial optimization problems. Fundamental and methodological aspects deal with runtime analysis, the structural properties of fitness landscapes, the study of metaheuristics core components, the clever design of their search principles, and their careful selection and configuration. Applications cover domains such as scheduling, routing, partitioning and general graph problems.
(source: Nielsen Book Data)

• NEW DEVELOPMENTS IN EVOLUTIONARY COMPUTATION RESEARCH; NEW DEVELOPMENTS IN EVOLUTIONARY COMPUTATION RESEARCH; LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA; CONTENTS; PREFACE;
• Chapter 1: MULTI-OBJECTIVE OPTIMIZATIONOF TRADING STRATEGIES USING GENETICALGORITHMS IN UNSTABLE ENVIRONMENTS; Abstract; A. Part A. Review of the Main Problem Solving and Optimization Techniques; B. Part B.A Review of Main Multi-Objective Optimization Techniques; C. Part C.A Case Study; Conclusion; References.

• Chapter 2: PROMOTING BETTER GENERALISATION IN MULTI-LAYER PERCEPTRONS USING A SIMULATED SYNAPTIC DOWNSCALING MECHANISMAbstract; 1. Introduction; 2. Background; 3. Model and Experiments; 4. Results and Analysis; 5. Conclusion; Acknowledgment; References;
• Chapter 3: PLANT PROPAGATION-INSPIRED ALGORITHMS; Abstract; 1. Introduction; 2. Background; 3. Plant Propagation Algorithms; 4. Applications; 5. Conclusion; References;
• Chapter 4: TOPOGRAPHICAL CLEARING DIFFERENTIAL EVOLUTION APPLIED TO REAL-WORLD MULTIMODAL OPTIMIZATION PROBLEMS; Abstract; 1. Introduction.

• 2. The Differential Evolution Algorithm3. Topographical Clearing; 4. Numerical Comparisons; 5. Conclusion; Appendix A. Nonlinear Systems Formulated as Optimization Problems; Appendix B. Data and Fitted Variables for the Catalytic Reactor Model; References;
• Chapter 5: ROBOTICS, EVOLUTION AND INTERACTIVITY IN SONIC ART INSTALLATIONS; Abstract; Introduction; 1. JaVOX, an Evolutionary Composition System; 2. Generative Sonification; 3. Automation x Interactivity; 4. Interactivity, Evolution and Structure; Conclusion; Acknowledgments; References.

• Chapter
• 6: AN ANALYSIS OF EVOLUTIONARY-BASED SAMPLING METHODOLOGIESAbstract;
• 1. Introduction;
• 2. Background;
• 3. Numerical Experiments: Design and Implementation;
• 4. Results and Discussion;
• 5. Conclusion; References; Blank Page; INDEX.

A common approach for solving simulation-driven engineering problems is by using metamodel-assisted optimization algorithms, namely, in which a metamodel approximates the computationally expensive simulation and provides predicted values at a lower computational cost. Such algorithms typically generate an initial sample of solutions which are then used to train a preliminary metamodel and to initiate an optimization process. One approach for generating the initial sample is with the design of experiment methods which are statistically oriented, while the more recent search-driven sampling approach invokes a computational intelligence optimizer such as an evolutionary algorithm, and then uses the vectors it generated as the initial sample. This book discusses research and new developments on evolutionary computation.
(source: Nielsen Book Data)

• Fundamentals. Evolutionary Dynamic Optimization: Test and Evaluation Environments / Shengxiang Yang, Trung Thanh Nguyen, Changhe Li
• Evolutionary Dynamic Optimization: Methodologies / Trung Thanh Nguyen [and others]
• Evolutionary Dynamic Optimization: Challenges and Perspectives / Philipp Rohlfshagen, Xin Yao
• Dynamic Multi-objective Optimization: A Survey of the State-of-the-Art / Carlo Raquel, Xin Yao
• Algorithm Design. A Comparative Study on Particle Swarm Optimization in Dynamic Environments / Changhe Li, Shengxiang Yang
• Memetic Algorithms for Dynamic Optimization Problems / Hongfeng Wang, Shengxiang Yang
• BIPOP: A New Algorithm with Explicit Exploration/Exploitation Control for Dynamic Optimization Problems / Enrique Alba [and others]
• Evolutionary Optimization on Continuous Dynamic Constrained Problems -- An Analysis / Trung Thanh Nguyen, Xin Yao
• Theoretical Analysis. Theoretical Advances in Evolutionary Dynamic Optimization / Philipp Rohlfshagen, Per Kristian Lehre, Xin Yao
• Analyzing Evolutionary Algorithms for Dynamic Optimization Problems Based on the Dynamical Systems Approach / Renato Tinós, Shengxiang Yang
• Dynamic Fitness Landscape Analysis / Hendrik Richter
• Dynamics in the Multi-objective Subset Sum: Analysing the Behavior of Population Based Algorithms / Iulia Maria Comsa, Crina Grosan, Shengxiang Yang
• Applications. Ant Colony Optimization Algorithms with Immigrants Schemes for the Dynamic Travelling Salesman Problem / Michalis Mavrovouniotis, Shengxiang Yang
• Genetic Algorithms for Dynamic Routing Problems in Mobile Ad Hoc Networks / Hui Cheng, Shengxiang Yang
• Evolutionary Computation for Dynamic Capacitated Arc Routing Problem / Yi Mei, Ke Tang, Xin Yao
• Evolutionary Algorithms for the Multiple Unmanned Aerial Combat Vehicles Anti-ground Attack Problem in Dynamic Environments / Xingguang Peng [and others]
• Advanced Planning in Vertically Integrated Wine Supply Chains / Maksud Ibrahimov [and others].

This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems. The motivation for this book arises from the fact that many real-world optimization problems and engineering systems are subject to dynamic environments, where changes occur over time. Key issues for addressing dynamic optimization problems in evolutionary computation, including fundamentals, algorithm design, theoretical analysis, and real-world applications, are presented. "Evolutionary Computation for Dynamic Optimization Problems" is a valuable reference to scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, nature- and bio-inspired computing, and evolutionary computation

• Bio-inspired Optimization Methods
• Topics Organization
• Evolutionary Computation
• Evolutionary Computation Methods
• Properties of Genetic Algorithms
• Evolving Regression Models
• Identification
• Parameter Estimation
• Independent Component Analysis
• Time Series Linear and Nonlinear Models
• Models of Time Series
• Autoregressive Moving Average Models
• Nonlinear Models
• Design of Experiments
• Experiments and Design of Experiments.

• The Evolutionary Design of Experiments
• The Evolutionary Model-Based Experimental Design: The Statistical Models in the Evolution
• Outliers
• Outliers in Independent Data
• Outliers in Time Series
• Genetic Algorithms for Multiple Outlier Detection
• Cluster Analysis
• Partitioning Problem
• Genetic Clustering Algorithms
• Fuzzy Partition
• Multivariate Mixture Models Estimation by Evolutionary Computing
• Genetic Algorithms in Classification and Regression Trees Models
• Clusters of Time Series and Directional Data
• Multiobjective Genetic ClusteringReferences.

This proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and statistical techniques. Modern statistical research is on the threshold of solving increasingly complex problems in high dimensions, and the generalization of its methodology to parameters whose estimators do not follow mathematically simple distributions is underway. Many of these challenges involve optimizing functions for which analytic solutions are infeasible. Evolutionary algorithms represent a powerful and easily understood means of approximating the optimum value in a variety of settings. The proposed text seeks to guide readers through the crucial issues of optimization problems in statistical settings and the implementation of tailored methods (including both stand-alone evolutionary algorithms and hybrid crosses of these procedures with standard statistical algorithms like Metropolis-Hastings) in a variety of applications. -- Provided by publisher.

This book constitutes the refereed proceedings of the 5th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2005, held in Lausanne, Switzerland in March/April 2005.The 24 revised full papers presented were carefully reviewed and selected from 66 submissions. The papers cover evolutionary algorithms as well as related approaches like scatter search, simulated annealing, ant colony optimization, immune algorithms, variable neighborhood search, hyperheuristics, and estimation of distribution algorithms. The papers deal with representations, analysis of operators and fitness landscapes, and comparison algorithms. Among the combinatorial optimization problems studied are graph coloring, quadratic assignment, knapsack, graph matching, packing, scheduling, timetabling, lot-sizing, and the traveling salesman problem.
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• Introduction
• A Swarm Global Optimization Algorithm Inspired in the Behavior of the Social-spider.-A States of Matter Algorithm for Global Optimization
• An Algorithm for Global Optimization Inspired by Collective Animal Behavior
• A Bio-inspired Evolutionary Algorithm: Allostatic Optimization
• Optimization Based on the Behavior of Locust Swarms.

The goal of this book is to present advances that discuss alternative Evolutionary Computation (EC) developments and non-conventional operators which have proved to be effective in the solution of several complex problems. The book has been structured so that each chapter can be read independently from the others. The book contains nine chapters with the following themes: 1) Introduction, 2) the Social Spider Optimization (SSO), 3) the States of Matter Search (SMS), 4) the collective animal behavior (CAB) algorithm, 5) the Allostatic Optimization (AO) method, 6) the Locust Search (LS) algorithm, 7) the Adaptive Population with Reduced Evaluations (APRE) method, 8) the multimodal CAB, 9) the constrained SSO method.

• pt. I. Exploiting multiple objectives : from problems to solutions
• pt. II. Machine learning with multiple objectives
• pt. III. Multiple objectives in design and engineering
• pt. IV. Scaling up multiobjective optimization.

• 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.
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This book provides a clear and comprehensive introduction to the field of evolutionary computation that takes an integrated approach. Evolutionary computation, the use of evolutionary systems as computational processes for solving complex problems, is a tool used by computer scientists and engineers who want to harness the power of evolution to build useful new artefacts, by biologists interested in developing and testing better models of natural evolutionary systems, and by artificial life scientists for designing and implementing new artificial evolutionary worlds. In this clear and comprehensive introduction to the field, Kenneth De Jong presents an integrated view of the state of the art in evolutionary computation. Although other books have described such particular areas of the field as genetic algorithms, genetic programming, evolution strategies, and evolutionary programming, "Evolutionary Computation" is noteworthy for considering these systems as specific instances of a more general class of evolutionary algorithms. This useful overview of a fragmented field is suitable for classroom use or as a reference for computer scientists and engineers.
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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.
• (source: Nielsen Book Data)

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

• Setting the stage for structured populations
• Island models
• Island models: empirical properties
• Lattice cellular models
• Lattice cellular models: empirical properties
• Random and irregular cellular populations
• Coevolutionary structured models
• Some nonconventional models
• Implementation notes.

As information handling systems get more and more complex, it becomes increasingly difficult to manage them using traditional approaches based on centralized and pre-defined control mechanisms. Over recent years, there has been a significant increase in taking inspiration from biology, the physical world, chemistry, and social systems to more efficiently manage such systems - generally based on the concept of self-organisation; this gave rise to self-organising applications. This book constitutes a reference and starting point for establishing the field of engineering self-organising applications. It comprises revised and extended papers presented at the Engineering Self-Organising Applications Workshop, ESOA 2003, held at AAMAS 2003 in Melbourne, Australia, in July 2003 and selected invited papers from leading researchers in self-organisation. The book is organized in parts on applications, natural metaphors (multi-cells and genetic algorithms, stigmergy, and atoms and evolution), artificial interaction mechanisms, middleware, and methods and tools.
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• EvoCOMNET contributions
• EvoFIN contributions
• EvoIASP contributions -- EvoINTERACTION contributions
• EvoMUSART contributions
• EvoSTOC contributions
• EvoTRANSLOG contributions.

This book constitutes the refereed joint proceedings of seven workshops on evolutionary computing, EvoWorkshops 2007, held in Valencia, Spain in April 2007. It examines evolutionary computation in communications, networks, and connected systems; finance and economics; image analysis and signal processing; and transportation and logistics. Coverage also details evolutionary algorithms in stochastic and dynamic environments.
(source: Nielsen Book Data)

• EvoBIO contributions
• EvoCOMNET contributions
• EvoHOT contributions
• EvoIASP contributions
• EvoINTERACTION contributions
• EvoMUSART contributions
• EvoSTOC contributions.

This book constitutes the refereed joint proceedings of seven workshops on evolutionary computing, EvoWorkshops 2006, held in Budapest, Hungary, in April 2006. The 65 revised full papers and 13 revised short papers presented were carefully reviewed and selected from a total of 149 submissions. In accordance with the seven workshops covered, the papers are organized in topical sections on evolutionary bioinformatics, evolutionary computation in communications, networks, and connected systems, evolutionary computation in hardware optimization, evolutionary computation in image analysis and signal processing, interactive evolution and humanized computational intelligence, evolutionary music and art, and evolutionary algorithms in stochastic and dynamic environments.
(source: Nielsen Book Data)

This book constitutes the refereed joint proceedings of six workshops on evolutionary computing, EvoWorkshops 2005, held in Lausanne, Switzerland in March/April 2005. The 56 revised full papers presented were carefully reviewed and selected from a total of 143 submissions. In accordance with the six workshops covered, the papers are organized in topical sections on evolutionary bioinformatics; evolutionary computing in communications, networks, and connected systems; hardware optimization techniques; evolutionary computation in image analysis and signal processing; evolutionary music and art; and evolutionary algorithms in stochastic and dynamic environments.
(source: Nielsen Book Data)

This book constitutes the joint refereed proceedings of six workshops on evolutionary computing, EvoWorkshops 2004, held together with EuroGP 2004 and EvoCOP 2004 in Coimbra, Portugal, in April 2004. The 55 revised full papers presented were carefully reviewed and selected from a total of 123 submissions. In accordance with the six workshops covered, the papers are organized in topical sections on evolutionary bioinformatics; evolutionary computing in communications, networks, and connected systems; hardware optimization techniques; evolutionary computing in image analysis and signal processing; evolutionary music and art; and evolutionary algorithms in stochastic and dynamic environments.
(source: Nielsen Book Data)

This book constitutes the joint refereed proceedings of six workshops, EvoWorkshops 2003, held together with EuroGP 2003 in Essex, UK in April 2003.The 63 revised full papers presented were carefully reviewed and selected from a total of 109 submissions. In accordance with the six workshops covered , the papers are organized in topical sections on bioinformatics, combinatorial optimization, image analysis and signal processing, evolutionary music and art, evolutionary robotics, and scheduling and timetabling.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of five application-oriented workshops held concurrently as EvoWorkshops 2001 in Como, Italy in April 2001. The 52 revised full papers presented were carefully reviewed and selected out of 75 submissions. The papers are organized in topical sections on graph problems, Knapsack problems, ant algorithms, assignment problems, evolutionary algorithms analysis, permutative problems, aeronautics, image analysis and signal processing, evolutionary learning, and evolutionary scheduling and timetabling.
(source: Nielsen Book Data)

• Simulating pricing behaviours using a genetic algorithm / Sue Bedingfield, Stephen Huxford, Yen Cheung
• Biologically inspired computational ecologies : a case study / Paul Devine, Ray Paton
• Modelling bounded rationality using evolutionary techniques / Bruce Edmonds, Scott Moss
• The abstract theory of evolution of the living / V.L. Kalmykov
• An evolutionary algorithm for single objective nonlinear constrained optimization problems / Hunter P. Albright, James P. Ignizio
• On recombinative sampling / Ian East
• The evolution of mutation, plasticity and culture in cyclically changing environments ; On the structure and transformation of landscapes / Tony Hirst
• Island model genetic algorithms and linearly separable problems / Darrell Whitley, Soraya Rana, Robert Heckendorn
• Empirical validation of the performance of a class of transient detector / Philip Jacob, Andrew Ball
• The construction and evaluation of decision trees : a comparison of evolutionary and concept learning methods / H. Kennedy ... [et al.]
• Parallel distributed genetic programming applied to the evolution of natural language recognisers / Riccardo Poli
• Scheduling planned maintenance of the South Wales region of the national grid / Bill Langdon
• Solving generic scheduling problems with a distributed genetic algorithm / Malcolm McIlhagga
• Directing the search of evolutionary and neighbourhood-search optimisers for the flowshop sequencing problem with an idle-time heuristic / Peter Ross, Andrew Tuson
• Multiobjective genetic algorithms for pump scheduling in water supply / Dragan Savic, Godfrey Walters, Martin Schwab
• Use of rules and preferences for schedule builders in genetic algorithms for production scheduling / Jane Shaw, Peter Fleming
• A voxel based approach to evolutionary shape optimisation / Peter Baron ... [et al.]
• Phase transition networks : a modelling technique supporting the evolution of autonomous agents' tactical and operational activities / Anthony Deakin, Derek Yates
• An evolutionary, agent-assisted strategy for conceptual design space decomposition / Ian Parmee, M. Beck
• Task scheduling with use of classifier systems / Franciszek Seredýnski.

This book constitutes the refereed post-workshop proceedings of the AISB International Workshop on Evolutionary Computing, held in Manchester, UK, in April 1997.The 22 strictly reviewed and revised full papers presented were selected for inclusion in the book after two rounds of refereeing. The papers are organized in sections on evolutionary approaches to issues in biology and economics, problem structure and finite landscapes, evolutionary machine learning and classifier systems, evolutionary scheduling, and more techniques and applications of evolutionary algorithms.
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• Session 1. Evolving digital circuits
• session 2. Artificial development
• session 3. GPU platforms for bio-inspired algorithms
• session 4. Implementations and applications of neural networks
• session 5. Test, repair and reconfiguration using evolutionary algorithms
• session 6. Applications of evolutionary algorithms in hardware
• session 7. Reconfigurable hardware platforms
• session 8. Applications of evolution to technology
• session 9. Novel methods in evolutionary design.

Annotation This book constitutes the refereed proceedings of the 9th International Conference on Evolvable Systems, ICES 2010, held in York, UK, in September 2010. The papers are organized in topical sections on evolving digital circuits; artificial development; GPU platforms for bio-inspired algorithms; implementations and applications of neural networks; test, repair and reconfiguration using evolutionary algorithms; applications of evolutionary algorithms in hardware; reconfigurable hardware platforms; applications of evolution to technology; and novel methods in evolutionary design.

• Computational Models of Evolution and Development
• Analysis of Gene Regulatory Networks
• Evolutionary Synthesis of Gene Regulatory Dynamics
• Evolution of Morphological Development
• Evolution of Neural Development
• Computational Brain-Body Co-Evolution
• Evolutionary Morphogenetic Self-Organization of Swarm Robots
• Towards Evolutionary Developmental Systems

This book provides a basic yet unified overview of theory and methodologies for evolutionary developmental systems. Based on the authors extensive research into the synergies between various approaches to artificial intelligence including evolutionary computation, artificial neural networks, and systems biology, it also examines the inherent links between biological intelligence and artificial intelligence. The book begins with an introduction to computational algorithms used to understand and simulate biological evolution and development, including evolutionary algorithms, gene regulatory network models, multi-cellular models for neural and morphological development, and computational models of neural plasticity. Chap. 2 discusses important properties of biological gene regulatory systems, including network motifs, network connectivity, robustness and evolvability. Going a step further, Chap. 3 presents methods for synthesizing regulatory motifs from scratch and creating more complex regulatory dynamics by combining basic regulatory motifs using evolutionary algorithms. Multi-cellular growth models, which can be used to simulate either neural or morphological development, are presented in Chapters 4 and 5. Chap. 6 examines the synergies and coupling between neural and morphological evolution and development. In turn, Chap. 7 provides preliminary yet promising examples of how evolutionary developmental systems can help in self-organized pattern generation, referred to as morphogenetic self-organization, highlighting the great potentials of evolutionary developmental systems. Finally, Chap. 8 rounds out the book, stressing the importance and promise of the evolutionary developmental approach to artificial intelligence. Featuring a wealth of diagrams, graphs and charts to aid in comprehension, this book offers a valuable asset for graduate students, researchers and practitioners who are interested in pursuing a different approach to artificial intelligence

• Intro
• Preface
• Contents
• Part I Background
• 1 Introduction
• 1.1 Optimization
• 1.2 Evolutionary Optimization
• 1.3 Evolutionary Multi-Task Optimization
• 1.4 Organization of the Book
• 2 Overview and Application-Driven Motivations of Evolutionary Multitasking
• 2.1 An Overview of EMT Algorithms
• 2.2 EMT in Real-World Problems
• 2.2.1 Category 1: EMT in Data Science Pipelines
• 2.2.2 Category 2: EMT in Evolving Embodied Intelligence
• 2.2.3 Category 3: EMT in Unmanned Systems Planning
• 2.2.4 Category 4: EMT in Complex Design

• 2.2.5 Category 5: EMT in Manufacturing, Operations Research
• 2.2.6 Category 6: EMT in Software and Services Computing
• Part II Evolutionary Multi-Task Optimization for Solving Continuous Optimization Problems
• 3 The Multi-Factorial Evolutionary Algorithm
• 3.1 Algorithm Design and Details
• 3.1.1 Multi-Factorial Optimization
• 3.1.2 Similarity and Difference Between Multi-factorial Optimization and Multi-Objective Optimization
• 3.1.3 The Multi-Factorial Evolutionary Algorithm
• 3.1.3.1 Population Initialization
• 3.1.3.2 Genetic Mechanisms
• 3.1.3.3 Selective Evaluation

• 3.1.3.4 Selection Operation
• 3.1.3.5 Summarizing the Salient Features of the MFEA
• 3.2 Empirical Study
• 3.2.1 Multitasking Across Functions with Intersecting Optima
• 3.2.2 Multitasking Across Functions with Separated Optima
• 3.2.3 Discussions
• 3.3 Summary
• 4 Multi-Factorial Evolutionary Algorithm with Adaptive Knowledge Transfer
• 4.1 Algorithm Design and Details
• 4.1.1 Representative Crossover Operators for Continuous Optimization
• 4.1.2 Knowledge Transfer via Different Crossover Operators in MFEA
• 4.1.3 MFEA with Adaptive Knowledge Transfer

• 4.1.3.1 Adaptive Assortative Mating and Adaptive Vertical Cultural Transmission
• 4.1.3.2 Adaptation of Transfer Crossover Indicators
• 4.2 Empirical Study
• 4.2.1 Experimental Setup
• 4.2.2 Performance Metric
• 4.2.3 Results and Discussions
• 4.2.3.1 Common Multi-Task Benchmarks
• 4.2.3.2 Complex Multi-Task Problems
• 4.2.4 Other Issues
• 4.3 Summary
• 5 Explicit Evolutionary Multi-Task Optimization Algorithm
• 5.1 Algorithm Design and Details
• 5.1.1 Denoising Autoencoder
• 5.1.2 The Explicit EMT Paradigm
• 5.1.2.1 Learning of Task Mapping

• 5.1.2.2 Explicit Genetic Transfer Across Tasks
• 5.2 Empirical Study
• 5.2.1 Single-Objective Multi-Task Optimization
• 5.2.1.1 Experiment Setup
• 5.2.1.2 Results and Discussions
• 5.2.2 Multi-Objective Multi-Task Optimization
• 5.2.2.1 Experiment Setup
• 5.2.2.2 Results and Discussions
• 5.3 Summary
• Part III Evolutionary Multi-Task Optimization for Solving Combinatorial Optimization Problems
• 6 Evolutionary Multi-Task Optimization for Generalized Vehicle Routing Problem with Occasional Drivers

A remarkable facet of the human brain is its ability to manage multiple tasks with apparent simultaneity. Knowledge learned from one task can then be used to enhance problem-solving in other related tasks. In machine learning, the idea of leveraging relevant information across related tasks as inductive biases to enhance learning performance has attracted significant interest. In contrast, attempts to emulate the human brains ability to generalize in optimization particularly in population-based evolutionary algorithms have received little attention to date. Recently, a novel evolutionary search paradigm, Evolutionary Multi-Task (EMT) optimization, has been proposed in the realm of evolutionary computation. In contrast to traditional evolutionary searches, which solve a single task in a single run, evolutionary multi-tasking algorithm conducts searches concurrently on multiple search spaces corresponding to different tasks or optimization problems, each possessing a unique function landscape. By exploiting the latent synergies among distinct problems, the superior search performance of EMT optimization in terms of solution quality and convergence speed has been demonstrated in a variety of continuous, discrete, and hybrid (mixture of continuous and discrete) tasks. This book discusses the foundations and methodologies of developing evolutionary multi-tasking algorithms for complex optimization, including in domains characterized by factors such as multiple objectives of interest, high-dimensional search spaces and NP-hardness.

Multi-objective optimization problems (MOPs) and uncertain optimization problems (UOPs) which widely exist in real life are challengeable problems in the fields of decision making, system designing, and scheduling, amongst others. Decomposition exploits the ideas of aEURO~making things simpleaEURO (TM) and aEURO~divide and conqueraEURO (TM) to transform a complex problem into a series of simple ones with the aim of reducing the computational complexity. In order to tackle the abovementioned two types of complicated optimization problems, this book introduces the decomposition strategy and conducts a systematic study to perfect the usage of decomposition in the field of multi-objective optimization, and extend the usage of decomposition in the field of uncertain optimization.
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• part 1. Theory and applications in engineering systems
• part 2. Theory and applications of single objective optimization studies
• part 3. Theory and applications of single and multiobjective optimization studies

• Methodology.- Memetic Algorithms in Planning, Scheduling, and Timetabling.- Landscapes, Embedded Paths and Evolutionary Scheduling.- Classical and Non-Classical Models of Production Scheduling.- Scheduling of Flow-Shop, Job-Shop, and Combined Scheduling Problems using MOEAs with Fixed and Variable Length Chromosomes.- Designing Dispatching Rules to Minimize Total Tardiness.- A Robust Meta-Hyper-Heuristic Approach to Hybrid Flow-Shop Scheduling.- Hybrid Particle Swarm Optimizers in the Single Machine Scheduling Problem: An Experimental Study.- An Evolutionary Approach for Solving the Multi-Objective Job-Shop Scheduling Problem.- Timetabling.- Multi-Objective Evolutionary Algorithm for University Class Timetabling Problem.- Metaheuristics for University Course Timetabling.- Energy Applications.- Optimum Oil Production Planning using an Evolutionary Approach.- A Hybrid Evolutionary Algorithm for Service Restoration in Power Distribution Systems.- Particle Swarm Optimisation for Operational Planning: Unit Commitment and Economic Dispatch.- Evolutionary Generator Maintenance Scheduling in Power Systems.- Networks.- Evolvable Fuzzy Scheduling Scheme for Multiple-ChannelPacket Switching Network.- A Multi-Objective Evolutionary Algorithm for Channel Routing Problems.- Transport.- Simultaneous Planning and Scheduling for Multi-Autonomous Vehicles.- Scheduling Production and Distribution of Rapidly Perishable Materials with Hybrid GA's.- A Scenario-based Evolutionary Scheduling Approach for Assessing Future Supply Chain Fleet Capabilities.- Business.- Evolutionary Optimization of Business Process Designs.- Using a Large Set of Low Level Heuristics in a Hyperheuristic Approach to Personnel Scheduling.- A Genetic-Algorithm-Based Reconfigurable Scheduler.- Evolutionary Algorithm for an Inventory Location Problem.
• (source: Nielsen Book Data)

Evolutionary scheduling is a vital research domain at the interface of artificial intelligence and operational research. This edited book gives an overview of many of the current developments in the large and growing field of evolutionary scheduling. It demonstrates the applicability of evolutionary computational techniques to solve scheduling problems, not only to small-scale test problems, but also fully-fledged real-world problems.
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• 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.
• (source: Nielsen Book Data)

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

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

"In this revised and significantly expanded second edition, distinguished scientist Dr. David B. Fogel presents the latest advances in both the theory and practice of evolutionary computation to help you keep pace with the most recent developments in this fast-changing field. In-depth and updated, EVOLUTIONARY COMPUTATION shows you how to use simulated evolution to achieve machine intelligence. You will gain current insights into the history of evolutionary computation and the newest theories shaping research today. Fogel thoroughly reviews the ""no free lunch"" theorem and includes a discussion of findings that challenge the very foundations of simulated evolution. This second edition also presents the latest game-playing techniques that combine evolutionary algorithms with neural networks, including their success in playing competitive checkers. Chapter by chapter, this comprehensive book highlights the relationship between learning and intelligence. EVOLUTIONARY COMPUTATION features an unparalleled integration of history with state-of-the-art theory and practice for engineers, professors, and graduate students of evolutionary computation and computer science who need to keep up to date in this developing field." Sponsored by: IEEE Neural Networks Council.
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"Science has made great strides in modeling space, time, mass and energy. Yet little attention has been paid to the precise representation of the information ubiquitous in nature. Introduction to Evolutionary Informatics fuses results from complexity modeling and information theory that allow both meaning and design difficulty in nature to be measured in bits. Built on the foundation of a series of peer-reviewed papers published by the authors, the book is written at a level easily understandable to readers with knowledge of rudimentary high school math. Those seeking a quick first read or those not interested in mathematical detail can skip marked sections in the monograph and still experience the impact of this new and exciting model of nature's information. This book is written for enthusiasts in science, engineering and mathematics interested in understanding the essential role of information in closely examined evolution theory."--Publisher's website.

• Part I Foundations
• Part II Advanced Optimization
• Part III Learning
• Part IV Appendix.

Practical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal, and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima. A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel parameters of the Nadaraya-Watson estimator, and a swarm-based iterative approach is presented for optimizing latent points in dimensionality reduction problems. Experiments on typical benchmark problems as well as numerous figures and diagrams illustrate the behavior of the introduced concepts and methods.

• Theory of Evolutionary Computing (I).- Optimal Fixed and Adaptive Mutation Rates for the LeadingOnes Problem.- Mirrored Sampling and Sequential Selection for Evolution Strategies.- Optimisation and Generalisation: Footprints in Instance Space.- Adaptive Drift Analysis.- Optimizing Monotone Functions Can Be Difficult.- Log-Linear Convergence of the Scale-Invariant (?/? w , ?)-ES and Optimal ? for Intermediate Recombination for Large Population Sizes.- Exploiting Overlap When Searching for Robust Optima.- Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis.- One-Point Geometric Crossover.- When Does Dependency Modelling Help? Using a Randomized Landscape Generator to Compare Algorithms in Terms of Problem Structure.- First-Improvement vs. Best-Improvement Local Optima Networks of NK Landscapes.- Differential Mutation Based on Population Covariance Matrix.- General Lower Bounds for the Running Time of Evolutionary Algorithms.- A Binary Encoding Supporting Both Mutation and Recombination.- Towards Analyzing Recombination Operators in Evolutionary Search.- Theory of Evolutionary Computing (II).- Bidirectional Relation between CMA Evolution Strategies and Natural Evolution Strategies.- A Fine-Grained View of GP Locality with Binary Decision Diagrams as Ant Phenotypes.- Drift Analysis with Tail Bounds.- More Effective Crossover Operators for the All-Pairs Shortest Path Problem.- Comparison-Based Adaptive Strategy Selection with Bandits in Differential Evolution.- Fixed Parameter Evolutionary Algorithms and Maximum Leaf Spanning Trees: A Matter of Mutation.- An Archive Maintenance Scheme for Finding Robust Solutions.- Experimental Supplements to the Theoretical Analysis of Migration in the Island Model.- General Scheme for Analyzing Running Times of Parallel Evolutionary Algorithms.- Negative Drift in Populations.- Log(?) Modifications for Optimal Parallelism.- The Linkage Tree Genetic Algorithm.- An Analysis of the XOR Dynamic Problem Generator Based on the Dynamical System.- The Role of Degenerate Robustness in the Evolvability of Multi-agent Systems in Dynamic Environments.- Machine Learning, Classifier Systems, Image Processing.- Evolutionary Learning of Technical Trading Rules without Data-Mining Bias.- Using Computational Intelligence to Identify Performance Bottlenecks in a Computer System.- Selecting Small Audio Feature Sets in Music Classification by Means of Asymmetric Mutation.- Globally Induced Model Trees: An Evolutionary Approach.- Open-Ended Evolutionary Robotics: An Information Theoretic Approach.- A Novel Similarity-Based Crossover for Artificial Neural Network Evolution.- Indirect Encoding of Neural Networks for Scalable Go.- Comparison-Based Optimizers Need Comparison-Based Surrogates.- A Cooperative Coevolutionary Approach to Partitional Clustering.- Feature Selection for Multi-purpose Predictive Models: A Many-Objective Task.- Incorporating Domain Knowledge into Evolutionary Computing for Discovering Gene-Gene Interaction.- The Application of Pittsburgh-Style Learning Classifier Systems to Address Genetic Heterogeneity and Epistasis in Association Studies.- Threshold Selection, Mitosis and Dual Mutation in Cooperative Co-evolution: Application to Medical 3D Tomography.- Comparative Analysis of Search and Score Metaheuristics for Bayesian Network Structure Learning Using Node Juxtaposition Distributions.- Analyzing the Credit Default Swap Market Using Cartesian Genetic Programming.- Memetic Algorithms, Hybridized Techniques, Meta and Hyperheurisics.- A Memetic Cooperative Optimization Schema and Its Application to the Tool Switching Problem.- Ownership and Trade in Spatial Evolutionary Memetic Games.- A Hyper-Heuristic Approach to Strip Packing Problems.- Asymptotic Analysis of Computational Multi-Agent Systems.- Path-Guided Mutation for Stochastic Pareto Local Search Algorithms.- Scheduling English Football Fixtures over the Holiday Period Using Hyper-heuristics.- Graph Clustering Based Model Building.- How to Choose Solutions for Local Search in Multiobjective Combinatorial Memetic Algorithms.- Secure and Task Abortion Aware GA-Based Hybrid Metaheuristics for Grid Scheduling.- A Memetic Algorithm for the Pickup and Delivery Problem with Time Windows Using Selective Route Exchange Crossover.- Ant Based Hyper Heuristics with Space Reduction: A Case Study of the p-Median Problem.- A Study of Multi-parent Crossover Operators in a Memetic Algorithm.- A Hybrid Genetic Algorithm for the Traveling Salesman Problem Using Generalized Partition Crossover.- A Memetic Algorithm with Non Gradient-Based Local Search Assisted by a Meta-model.- Multiobjective Optimization, Theoretical Aspects.- Theoretically Investigating Optimal ?-Distributions for the Hypervolume Indicator: First Results for Three Objectives.- Convergence Rates of (1+1) Evolutionary Multiobjective Optimization Algorithms.- Tight Bounds for the Approximation Ratio of the Hypervolume Indicator.- Evolutionary Multiobjective Optimization Algorithm as a Markov System.- A Natural Evolution Strategy for Multi-objective Optimization.- Solving Multiobjective Optimization Problem by Constraint Optimization.- Enhancing Diversity for Average Ranking Method in Evolutionary Many-Objective Optimization.- Objective Space Partitioning Using Conflict Information for Many-Objective Optimization.- How Crossover Speeds Up Evolutionary Algorithms for the Multi-criteria All-Pairs-Shortest-Path Problem.- Path Relinking on Many-Objective NK-Landscapes.- In Search of Equitable Solutions Using Multi-objective Evolutionary Algorithms.- Stopping Criteria for Genetic Algorithms with Application to Multiobjective Optimization.- Defining and Optimizing Indicator-Based Diversity Measures in Multiobjective Search.- On Expected-Improvement Criteria for Model-based Multi-objective Optimization.- Parameter Tuning Boosts Performance of Variation Operators in Multiobjective Optimization.
• (source: Nielsen Book Data)

We are very pleased to present to you this LNCS volume, the proceedings of the 11th International Conference on Parallel Problem Solving from Nature (PPSN 2010). PPSN is one of the most respected and highly regarded c- ference series in evolutionary computation, and indeed in natural computation aswell.Thisbiennialeventwas?rstheldinDortmundin1990, andtheninBr- sels (1992), Jerusalem (1994), Berlin (1996), Amsterdam (1998), Paris (2000), Granada (2002), Birmingham (2004), Reykjavik (2006) and again in Dortmund in 2008. PPSN 2010 received 232 submissions. After an extensive peer review p- cess involving more than 180 reviewers, the program committee chairs went through all the review reports and ranked the papers according to the revi- ers'comments. Each paper wasevaluated by at least three reviewers.Additional reviewers from the appropriate branches of science were invoked to review into disciplinary papers. The top 128 papers were ?nally selected for inclusion in the proceedings and presentation at the conference. This represents an acceptance rate of 55%, which guarantees that PPSN will continue to be one of the c- ferences of choice for bio-inspired computing and metaheuristics researchers all over the world who value the quality over the size of a conference. The papers included in the proceedingsvolumes covera wide range of topics, fromevolutionarycomputationto swarmintelligence, frombio-inspiredcomp- ing to real-world applications. Machine learning and mathematical games s- portedbyevolutionaryalgorithmsaswellasmemetic, agent-orientedsystemsare also represented. They all are the latest and best in natural computation. The proceedings are composed of two volumes divided into nine thematic sections.
(source: Nielsen Book Data)