LEADER 05499nam 2200673Ia 450 001 9910786845303321 005 20230920172212.0 010 $a1-118-65956-2 010 $a1-118-65950-3 035 $a(CKB)2670000000386425 035 $a(EBL)1216196 035 $a(OCoLC)851316251 035 $a(SSID)ssj0000915662 035 $a(PQKBManifestationID)11487017 035 $a(PQKBTitleCode)TC0000915662 035 $a(PQKBWorkID)10869116 035 $a(PQKB)11619899 035 $a(MiAaPQ)EBC1216196 035 $a(Au-PeEL)EBL1216196 035 $a(CaPaEBR)ebr10722521 035 $a(CaONFJC)MIL499128 035 $a(OCoLC)859885568 035 $a(EXLCZ)992670000000386425 100 $a20130326d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aEvolutionary optimization algorithms$b[electronic resource] $ebiologically-Inspired and population-based approaches to computer intelligence /$fDan Simon 210 $aHoboken, NJ $cJohn Wiley & Sons Inc.$d2013 215 $a1 online resource (776 p.) 300 $aDescription based upon print version of record. 311 $a1-299-67878-5 311 $a0-470-93741-6 320 $aIncludes bibliographical references (p. 685-726) and index. 327 $aCover; Title Page; Copyright Page; SHORT TABLE OF CONTENTS; DETAILED TABLE OF CONTENTS; Acknowledgments; Acronyms; List of Algorithms; PART I INTRODUCTION TO EVOLUTIONARY OPTIMIZATION; 1 Introduction; 1.1 Terminology; 1.2 Why Another Book on Evolutionary Algorithms?; 1.3 Prerequisites; 1.4 Homework Problems; 1.5 Notation; 1.6 Outline of the Book; 1.7 A Course Based on This Book; 2 Optimization; 2.1 Unconstrained Optimization; 2.2 Constrained Optimization; 2.3 Multi-Objective Optimization; 2.4 Multimodal Optimization; 2.5 Combinatorial Optimization; 2.6 Hill Climbing 327 $a2.6.1 Biased Optimization Algorithms2.6.2 The Importance of Monte Carlo Simulations; 2.7 Intelligence; 2.7.1 Adaptation; 2.7.2 Randomness; 2.7.3 Communication; 2.7.4 Feedback; 2.7.5 Exploration and Exploitation; 2.8 Conclusion; Problems; PART II CLASSIC EVOLUTIONARY ALGORITHMS; 3 Genetic Algorithms; 3.1 The History of Genetics; 3.1.1 Charles Darwin; 3.1.2 Gregor Mendel; 3.2 The Science of Genetics; 3.3 The History of Genetic Algorithms; 3.4 A Simple Binary Genetic Algorithm; 3.4.1 A Genetic Algorithm for Robot Design; 3.4.2 Selection and Crossover; 3.4.3 Mutation; 3.4.4 GA Summary 327 $a3.4.5 GA Tuning Parameters and Examples3.5 A Simple Continuous Genetic Algorithm; 3.6 Conclusion; Problems; 4 Mathematical Models of Genetic Algorithms; 4.1 Schema Theory; 4.2 Markov Chains; 4.3 Markov Model Notation for Evolutionary Algorithms; 4.4 Markov Models of Genetic Algorithms; 4.4.1 Selection; 4.4.2 Mutation; 4.4.3 Crossover; 4.5 Dynamic System Models of Genetic Algorithms; 4.5.1 Selection; 4.5.2 Mutation; 4.5.3 Crossover; 4.6 Conclusion; Problems; 5 Evolutionary Programming; 5.1 Continuous Evolutionary Programming; 5.2 Finite State Machine Optimization 327 $a5.3 Discrete Evolutionary Programming5.4 The Prisoner's Dilemma; 5.5 The Artificial Ant Problem; 5.6 Conclusion; Problems; 6 Evolution Strategies; 6.1 The (1+1) Evolution Strategy; 6.2 The 1/5 Rule: A Derivation; 6.3 The (?+l) Evolution Strategy; 6.4 (? + ?) and (?, ?) Evolution Strategies; 6.5 Self-Adaptive Evolution Strategies; 6.6 Conclusion; Problems; 7 Genetic Programming; 7.1 Lisp: The Language of Genetic Programming; 7.2 The Fundamentals of Genetic Programming; 7.2.1 Fitness Measure; 7.2.2 Termination Criteria; 7.2.3 Terminal Set; 7.2.4 Function Set; 7.2.5 Initialization 327 $a7.2.6 Genetic Programming Parameters7.3 Genetic Programming for Minimum Time Control; 7.4 Genetic Programming Bloat; 7.5 Evolving Entities other than Computer Programs; 7.6 Mathematical Analysis of Genetic Programming; 7.6.1 Definitions and Notation; 7.6.2 Selection and Crossover; 7.6.3 Mutation and Final Results; 7.7 Conclusion; Problems; 8 Evolutionary Algorithm Variations; 8.1 Initialization; 8.2 Convergence Criteria; 8.3 Problem Representation Using Gray Coding; 8.4 Elitism; 8.5 Steady-State and Generational Algorithms; 8.6 Population Diversity; 8.6.1 Duplicate Individuals 327 $a8.6.2 Niche-Based and Species-Based Recombination 330 $aA 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, biog 606 $aEvolutionary computation 606 $aComputer algorithms 606 $aBiologically-inspired computing 615 0$aEvolutionary computation. 615 0$aComputer algorithms. 615 0$aBiologically-inspired computing. 676 $a006.3 686 $aMAT008000$2bisacsh 700 $aSimon$b Dan$f1960-$0856795 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910786845303321 996 $aEvolutionary optimization algorithms$93759332 997 $aUNINA