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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)

• 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)