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Self-Adaptive Heuristics for Evolutionary Computation Book

Self-Adaptive Heuristics for Evolutionary Computation
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  • Self-Adaptive Heuristics for Evolutionary Computation
  • Written by author Oliver Kramer
  • Published by Springer-Verlag New York, LLC, September 2008
  • Evolutionary algorithms are successful biologically inspired meta-heuristics. Their success depends on adequate parameter settings. The question arises: how can evolutionary algorithms learn parameters automatically during the optimization? Evolution
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Authors

1 Introduction 1

Pt. I Foundations of Evolutionary Computation

2 Evolutionary Algorithms 9

3 Self-Adaptation 29

Pt. II Self-Adaptive Operators

4 Biased Mutation for Evolution Strategies 51

5 Self-Adaptive Inversion Mutation 81

6 Self-Adaptive Crossover 97

Pt. III Constraint Handling

7 Constraint Handling Heuristics for Evolution Strategies 117

Pt. IV Summary

8 Summary and Conclusion 143

Pt. V Appendix

A Continuous Benchmark Functions 149

B Discrete Benchmark Functions 159

References 163

List of Figures 173

List of Tables 175

Index 179


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