Noisy Optimization With Evolution Strategies

  • Dirk V. Arnold

Part of the Genetic Algorithms and Evolutionary Computation book series (GENA, volume 8)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Dirk V. Arnold
    Pages 1-6
  3. Dirk V. Arnold
    Pages 7-20
  4. Dirk V. Arnold
    Pages 21-36
  5. Dirk V. Arnold
    Pages 37-52
  6. Dirk V. Arnold
    Pages 53-77
  7. Dirk V. Arnold
    Pages 79-96
  8. Dirk V. Arnold
    Pages 97-102
  9. Back Matter
    Pages 103-158

About this book

Introduction

Noise is a common factor in most real-world optimization problems. Sources of noise can include physical measurement limitations, stochastic simulation models, incomplete sampling of large spaces, and human-computer interaction. Evolutionary algorithms are general, nature-inspired heuristics for numerical search and optimization that are frequently observed to be particularly robust with regard to the effects of noise.

Noisy Optimization with Evolution Strategies contributes to the understanding of evolutionary optimization in the presence of noise by investigating the performance of evolution strategies, a type of evolutionary algorithm frequently employed for solving real-valued optimization problems. By considering simple noisy environments, results are obtained that describe how the performance of the strategies scales with both parameters of the problem and of the strategies considered. Such scaling laws allow for comparisons of different strategy variants, for tuning evolution strategies for maximum performance, and they offer insights and an understanding of the behavior of the strategies that go beyond what can be learned from mere experimentation.

This first comprehensive work on noisy optimization with evolution strategies investigates the effects of systematic fitness overvaluation, the benefits of distributed populations, and the potential of genetic repair for optimization in the presence of noise. The relative robustness of evolution strategies is confirmed in a comparison with other direct search algorithms.

Noisy Optimization with Evolution Strategies is an invaluable resource for researchers and practitioners of evolutionary algorithms.

Keywords

algorithms behavior evolution evolutionary algorithm heuristics human-computer interaction (HCI) optimization search algorithm simulation

Authors and affiliations

  • Dirk V. Arnold
    • 1
  1. 1.University of DortmundGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-1105-2
  • Copyright Information Kluwer Academic Publishers 2002
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-5397-3
  • Online ISBN 978-1-4615-1105-2
  • Series Print ISSN 1568-2587
  • About this book