Evolutionary Optimization Algorithms: Biologocally-inspired and Population-based

Description: Evolutionary Optimization Algorithms by Dan Simon This book is a clear and lucid presentation of Evolutionary Algorithms, with a straightforward, bottom-up approach that provides the reader with a firm grasp of the basic principles of EAs. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear—but theoretically rigorous—understanding of evolutionary algorithms, with an emphasis on implementationGives a careful treatment of recently developed EAs—including opposition-based learning, artificial fish swarms, bacterial foraging, and many others— and discusses their similarities and differences from more well-established EAsIncludes chapter-end problems plus a solutions manual available online for instructorsOffers simple examples that provide the reader with an intuitive understanding of the theoryFeatures source code for the examples available on the authors websiteProvides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science. Back Cover A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear but theoretically rigorous understanding of evolutionary algorithms, with an emphasis on implementation Gives a careful treatment of recently developed EAs including opposition-based learning, artificial fish swarms, bacterial foraging, and many others and discusses their similarities and differences from more well-established EAs Includes chapter-end problems plus a solutions manual available online for instructors Offers simple examples that provide the reader with an intuitive understanding of the theory Features source code for the examples available on the authors website Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science. Flap A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear--but theoretically rigorous--understanding of evolutionary algorithms, with an emphasis on implementation Gives a careful treatment of recently developed EAs--including opposition-based learning, artificial fish swarms, bacterial foraging, and many others-- and discusses their similarities and differences from more well-established EAs Includes chapter-end problems plus a solutions manual available online for instructors Offers simple examples that provide the reader with an intuitive understanding of the theory Features source code for the examples available on the authors website Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science. Author Biography DAN SIMON is a Professor at Cleveland State University in the Department of Electrical and Computer Engineering. His teaching and research interests include control theory, computer intelligence, embedded systems, technical writing, and related subjects. He is the author of the book Optimal State Estimation (Wiley). Table of Contents Acknowledgments xxi Acronyms xxiii List of Algorithms xxvii Part I: Introduction to Evolutionary Optimization 1 Introduction 1 2 Optimization 11 Part II: Classic Evolutionary Algorithms 3 Generic Algorithms 35 4 Mathematical Models of Genetic Algorithms 63 5 Evolutionary Programming 95 6 Evolution Strategies 117 7 Genetic Programming 141 8 Evolutionary Algorithms Variations 179 Part III: More Recent Evolutionary Algorithms 9 Simulated Annealing 223 10 Ant Colony Optimization 241 11 Particle Swarm Optimization 265 12 Differential Evolution 293 13 Estimation of Distribution Algorithms 313 14 Biogeography-Based Optimization 351 15 Cultural Algorithms 377 16 Opposition-Based Learning 397 17 Other Evolutionary Algorithms 421 Part IV: Special Type of Optimization Problems 18 Combinatorial Optimization 449 19 Constrained Optimization 481 20 Multi-Objective Optimization 517 21 Expensive, Noisy and Dynamic Fitness Functions 563 Appendices A Some Practical Advice 607 B The No Free Lunch Theorem and Performance Testing 613 C Benchmark Optimization Functions 641 References 685 Topic Index 727 Long Description A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear but theoretically rigorous understanding of evolutionary algorithms, with an emphasis on implementation Gives a careful treatment of recently developed EAs including opposition-based learning, artificial fish swarms, bacterial foraging, and many others and discusses their similarities and differences from more well-established EAs Includes chapter-end problems plus a solutions manual available online for instructors Offers simple examples that provide the reader with an intuitive understanding of the theory Features source code for the examples available on the authors website Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science. Details ISBN0470937416 Author Dan Simon Short Title EVOLUTIONARY OPTIMIZATION ALGO Language English ISBN-10 0470937416 ISBN-13 9780470937419 Media Book Format Hardcover Year 2013 Illustrations Yes Subtitle Biologocally-inspired and Population-based Approaches to Computer Intelligence Edition 1st Country of Publication United States UK Release Date 2013-05-17 AU Release Date 2013-04-12 NZ Release Date 2013-04-12 Pages 784 Publisher John Wiley & Sons Inc Publication Date 2013-05-17 Imprint John Wiley & Sons Inc Place of Publication New York DEWEY 519.6 Audience Professional & Vocational US Release Date 2013-05-17 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:51744321;

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