Evolutionary computation: a unified approach
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Mass. [u.a.]
MIT Press
2006
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| Schriftenreihe: | A Bradford book
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | IX, 256 S. graph. Darst. |
| ISBN: | 0262041944 9780262529600 |
Internformat
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| 264 | 1 | |a Cambridge, Mass. [u.a.] |b MIT Press |c 2006 | |
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Datensatz im Suchindex
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| adam_text | Contents 1 Introduction 1 1.1 Basic Evolutionary Processes.................... 2 1.2 EV: A Simple Evolutionary System.................................................................... 3 1.3 EV on a Simple Fitness Landscape.................................................................... 6 1.4 EV on a More Complex Fitness Landscape ..................................................... 15 1.5 Evolutionary Systems as Problem Solvers........................................................ 19 1.6 Exercises ................................................................................................................. 21 2 A Historical Perspective 23 2.1 Early Algorithmic Views......................................................................................... 23 2.2 The Catalytic 1960s............................................................................................... 24 2.3 The Explorative 1970s......................................................................................... 25 2.3.1 Evolutionary Programming........................................................................25 2.3.2 Evolution Strategies................................................................................... 25 2.3.3 Genetic Algorithms...................................................................................... 26 2.4 The Exploitative 1980s............................................................................................ 27 2.4.1 Optimization Applications...........................................................................27 2.4.2 Other EA
Applications .......................................................................... 28 2.4.3 Summary..................................................................................................... 28 2.5 The Unifying 1990s.................................................................................................. 29 2.6 The Twenty-first Century: Mature Expansion......................................................29 2.7 Summary................................................................................................................. 31 3 Canonical EvolutionaryAlgorithms 33 3.1 Introduction.............................................................................................................. 33 3.2 EV (m,n).................................................................... 33 3.3 Evolutionary Programming................................................................................... 34 3.4 Evolution Strategies............................................................................................... 36 3.5 Genetic Algorithms.................................................................................................. 40 3.5.1 Multi-parent Reproduction........................................................................41 3.5.2 Universal Genetic Codes..............................................................................43 3.6 Summary . ............................................................................................................... 47
Vi CONTENTS 4 A Unified View of Simple EAs 49 4.1 A Common Framework .......................................................................................... 49 4.2 Population Size..................................................................................................... 50 4.2.1 Parent Population Size m........................................................................ 50 4.2.2 Offspring Population Size n..................................................................... 52 4.3 Selection ................................................................................................................. 54 4.3.1 ChoosingSelection Mechanisms....................................................................58 4.3.2 Survival Selection: A Special Case......................................................... 59 4.3.3 Selection Summary.................................................................................... 60 4.4 Reproductive Mechanisms....................................................................................... 61 4.4.1 Mutation......................................................................................................61 4.4.2 Recombination......................................................................................... 63 4.4.3 Crossover or Mutation?.............................................................................. 66 4.4.4 Representation Issues ............................................................................. 67 4.4.5 Choosing Effective Reproductive
Mechanisms...........................................68 4.5 Summary.............................................................................................................. 69 5 Evolutionary Algorithms as Problem Solvers 7І Simple EAs as Parallel Adaptive Search............................................................. 71 5.1.1 Representation......................................................................................... 72 5.1.1.1 Fixed-Length Linear Objects................................................... 73 5.1.1.2 Nonlinear Objects......................................................................... 73 5.1.1.3 Variable-Length Objects ......................................................... 74 5.1.1.4 Nonlinear, Variable-Length Objects........................................ 75 5.1.2 Reproductive Operators........................................................................... 75 5.1.3 Objective Fitness Evaluation.................................................................. 76 5.1.4 Population Sizes and Dynamics............................................................... 77 5.1.5 Convergence and Stopping Criteria......................................................... 78 5.1.6 Returning an Answer .............................................................................. 79 5.1.7 Summary.................................................................................................. 80 5.2 EA-based Optimization...................................................................................... 80 5.2.1 OPT-
EAs.................................................................................................. 80 5.2.1.1 Fitness Scaling...............................................................................81 5.2.1.2 Convergence and Elitism............................................................. 82 5.2.1.3 Summary.................................................................................... 83 5.2.2 Parameter Optimization.............................................................................. 83 5.2.2.1 Phenotypic Representations and Operators........................... 84 5.2.2.2 Genotypic Representations and Operators.............................. 84 5.2.2.3 Choosing Representationsand Operators................................... 85 5.2.2.4 Real-Valued Parameter Optimization..................................... 85 5.2.2.5 Integer-Valued Parameter Optimization..................................... 93 5.2.2.6 Symbolic Parameter Optimization........................................... 95 5.2.2.7 Non-homogeneous Parameter Optimization...............................97 5.2.3 Constrained Optimization........................................................................ 97 5.2.4 Data Structure Optimization....................................................................100 5.1
CONTENTS 5.3 5.4 5.5 5.6 5.7 vii 5.2.4.1 Variable-Length Data Structures.............................................. 102 5.2.5 Multi-objective Optimization...................................................................103 5.2.6 Summary....................................................................................................104 EA-Based Search....................................................................................................105 EA-Based Machine Learning ...............................................................................107 EA-Based Automated Programming................................................................... 109 5.5.1 Representing Programs ............................................................................109 5.5.2 Evaluating Programs......................................................... 110 5.5.3 Summary....................................................................................................112 EA-Based Adaptation...........................................................................................112 Summary............................................................................................................... 113 6 Evolutionary Computation Theory 115 6.1 Introduction............................................................................................................ 115 6.2 Analyzing EA Dynamics........................................................................................117 6.3 Selection-Only
Models...........................................................................................120 6.3.1 Non-overlapping-Generation Models....................................................... 120 6.3.1.1 Uniform (Neutral) Selection .....................................................121 6.3.1.2 Fitness-Biased Selection..............................................................123 6.3.1.3 Non-overlapping-Generation Models with ո փ τη....................132 6.3.2 Overlapping-Generation Models................................................................ 134 6.3.2.1 Uniform (Neutral) Selection .....................................................135 6.3.2.2 Fitness-Biased Selection..............................................................136 6.3.3 Selection in Standard EAs.........................................................................137 6.3.4 Reducing Selection Sampling Variance.................................................... 138 6.3.5 Selection Summary.....................................................................................140 6.4 Reproduction-Only Models ..................................................................................141 6.4.1 Non-overlapping-Generation Models....................................................... 141 6.4.1.1 Reproduction for Fixed-Length Discrete Linear Genomes . . 143 6.4.1.2 Reproduction for Other Genome Types.................................. 152 6.4.2 Overlapping-Generation Models................................................................ 158 6.4.3 Reproduction
Summary............................................................................159 6.5 Selection and Reproduction Interactions............................................................. 160 6.5.1 Evolvability and Price’s Theorem............................................................. 160 6.5.2 Selection and Discrete Recombination.................................................... 162 6.5.2.1 Discrete Recombination from a Schema Perspective............162 6.5.2.2 Crossover-Induced Diversity .....................................................163 6.5.2.3 Crossover-Induced Fitness Improvements................................ 166 6.5.3 Selection and Other Recombination Operators ..................................... 169 6.5.4 Selection and Mutation............................................................................171 6.5.4.1 Mutation from a Schema Perspective..................................... 172 6.5.4.2 Mutation-Induced Diversity........................................................172 6.5.4.3 Mutation-Induced Fitness Improvements................................ 175 6.5.5 Selection and Other Mutation Operators................................................. 177 6.5.6 Selection and Multiple Reproductive Operators..................................... 177
CONTENTS viii 6.5.7 Selection, Reproduction, and Population Size .......................................... 180 6.5.7.1 Non-overlapping-Generation Models...........................................181 Ö.5.7.2 Overlapping-Generation Models.................................................183 6.5.8 Summary....................................................................................................... 185 6.6 Representation.......................................................................................................... 185 6.6.1 Capturing Important Application Features .............................................185 6.6.2 Defining Effective Reproduction Operators ............................................. 186 6.6.2.1 Effective Mutation Operators.......................................................187 6.6.2.2 Effective Recombination Operators ...........................................187 6.7 Landscape Analysis.................................................................................................... 188 6.8 Models of Canonical EAs ........................................................................................189 6.8.1 Infinite Population Models for Simple GAs .............................................189 6.8.2 Expected Value Models of Simple GAs......................................................191 6.8.2.1 GA Schema Theory...................................................................... 192 Θ.8.2.2 Summary......................................................................................... 199 6.8.3 Markov
Models.............................................................................................. 199 6.8.3.1 Markov Models of Finite Population EAs................................. 200 6.8.3.2 Markov Models of SimpleGAs.................................................... 201 6.8.3.3 Summary.........................................................................................203 6.8.4 Statistical Mechanics Models..................................................................... 203 6.8.5 Summary......................................................................................... 205 6.9 Application-Oriented Theories..................................................................................205 6.9.1 Optimization-Oriented Theories.................................................................. 205 6.9.1.1 Convergence and Rates of Convergence.................................... 206 6.9.1.2 ESs and Real-Valued Parameter Optimization Problems . . . 206 6.9.1.3 Simple EAs and DiscreteOptimization Problems......................207 6.9.1.4 Optimizing with Genetic Algorithms...........................................208 6.10 Summary..................................................................................................................209 7 Advanced EC Topics 211 7.1 Self-adapting EAs .................................................................................................... 211 7.1.1 Adaptation at EA Design Time.................................................................. 212 7.1.2 Adaptation over Multiple EA
Runs............................................................ 212 7.1.3 Adaptation during an EA Run.................................................................. 213 7.1.4 Summary....................................................................................................... 213 7.2 Dynamic Landscapes................................................................................................. 213 7.2.1 Standard EAs on Dynamic Landscapes ................................................... 214 7.2.2 Modified EAs for Dynamic Landscapes...................................................... 215 7.2.3 Categorizing Dynamic Landscapes............................................................ 216 7.2.4 The Importance of the Rate of Change...................................................... 216 7.2.5 The Importance of Diversity ......................................................................217 7.2.6 Summary........................................................................................................219 7.3 Exploiting Parallelism.............................................................................................. 219 7.3.1 Coarse-Grained Parallel EAs......................................................................220 7.3.2 Fine-Grained Models..................................................................................... 220
CONTENTS ix 7.3.3 Summary.................................................................................................... 221 7.4 Evolving Executable Objects...............................................................................221 7.4.1 Representation of Behaviors...................................................................... 221 7.4.2 Summary....................................................................................................223 7.5 Multi-objective EAs............................................................................................. 223 7.6 Hybrid EAs.............................................................................................................224 7.7 Biologically Inspired Extensions............................................................................225 7.7.1 Non-random Mating and Spéciation....................................................... 225 7.7.2 Coevolutionary Systems............................................................................226 7.7.2.1 CoEC Architectures...................................................................227 7.7.2.2 CoEC Dynamics.........................................................................227 7.7.3 Generative Representations andMorphogenesis.......................................228 7.7.4 Inclusion of Lamarckian Properties.......................................................... 229 7.7.5 Agent-Oriented Models ............................................................................229 7.7.6
Summary....................................................................................................230 7.8 Summary............................................................................................................... 230 8 The Road Ahead 231 8.1 Modeling General Evolutionary Systems.............................................................231 8.2 More Unification................................................................................................... 232 8.3 Summary............................................................................................................... 232 Appendix A: Source Code Overview EC1: A.1.1 A.l.2 A.2 EC2: A.3 EC3: A.4 EC4: 233 A Very Simple EC System........................................................................233 EC1 Code Structure.................................................................................. 234 EC1 Parameters........................................................................................ 235 A More Interesting EC System..................................................................236 A More Flexible EC System .....................................................................237 An EC Research System ...........................................................................240 Bibliography 241 Index 253 A.l
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| spelling | De Jong, Kenneth A. Verfasser aut Evolutionary computation a unified approach Kenneth A. De Jong Cambridge, Mass. [u.a.] MIT Press 2006 IX, 256 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier A Bradford book Computação bioinspirada larpcal Computação evolutiva larpcal Programmation évolutive Réseaux neuronaux à structure évolutive Evolutionary computation Evolutionary programming (Computer science) Evolutionärer Algorithmus (DE-588)4366912-8 gnd rswk-swf Evolutionärer Algorithmus (DE-588)4366912-8 s DE-604 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009481219&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | De Jong, Kenneth A. Evolutionary computation a unified approach Computação bioinspirada larpcal Computação evolutiva larpcal Programmation évolutive Réseaux neuronaux à structure évolutive Evolutionary computation Evolutionary programming (Computer science) Evolutionärer Algorithmus (DE-588)4366912-8 gnd |
| subject_GND | (DE-588)4366912-8 |
| title | Evolutionary computation a unified approach |
| title_auth | Evolutionary computation a unified approach |
| title_exact_search | Evolutionary computation a unified approach |
| title_full | Evolutionary computation a unified approach Kenneth A. De Jong |
| title_fullStr | Evolutionary computation a unified approach Kenneth A. De Jong |
| title_full_unstemmed | Evolutionary computation a unified approach Kenneth A. De Jong |
| title_short | Evolutionary computation |
| title_sort | evolutionary computation a unified approach |
| title_sub | a unified approach |
| topic | Computação bioinspirada larpcal Computação evolutiva larpcal Programmation évolutive Réseaux neuronaux à structure évolutive Evolutionary computation Evolutionary programming (Computer science) Evolutionärer Algorithmus (DE-588)4366912-8 gnd |
| topic_facet | Computação bioinspirada Computação evolutiva Programmation évolutive Réseaux neuronaux à structure évolutive Evolutionary computation Evolutionary programming (Computer science) Evolutionärer Algorithmus |
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