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Evolutionary optimization algorithms : biologically-Inspired and population-based approaches to computer intelligence

Author: Dan Simon
Publisher: Hoboken, New Jersey : John Wiley & Sons Inc., [2013]
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
"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. Covering the theory, history, mathematics, and applications of evolutionary optimization algorithms, this timely and practical book offers lengthy examples, a companion website, MATLAB code, and a Solutions Manual--making it
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Genre/Form: Electronic books
Electronic book
Additional Physical Format: Print version:
Simon, Dan, 1960-
Evolutionary optimization algorithms.
Hoboken, New Jersey : John Wiley & Sons Inc., [2013]
(DLC) 2013000458
(OCoLC)833144850
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Dan Simon
ISBN: 9781118659502 1118659503 9781299678781 1299678785
OCLC Number: 849936252
Description: 1 online resource
Contents: Acknowledgments xxi Acronyms xxiii List of Algorithms xxvii Pert I: Introduction to Evolutionary Optimization 1 Introduction 1 2 Optimization 11 Part II: Classic Evoluntionary 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 Oppostion-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 Luch Therorem and Performance Testing 613 C Benchmark Optimization Functions 641
Responsibility: Dan Simon.
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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.  Read more...

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