進化最適化アルゴリズム<br>Evolutionary Optimization Algorithms : Biologically-Inspired and Population-Based Approaches to Computer Intelligence

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進化最適化アルゴリズム
Evolutionary Optimization Algorithms : Biologically-Inspired and Population-Based Approaches to Computer Intelligence

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  • 製本 Hardcover:ハードカバー版/ページ数 742 p.
  • 言語 ENG
  • 商品コード 9780470937419
  • DDC分類 006.3

Full 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 author's 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.

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

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