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Noisy Optimization with Evolution Strategies

by Dirk V. Arnold

Volume 8
Genetic Algorithms and Evolutionary Computation Series
Kluwer Academic Publishers, 2002.


From the Back Cover

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.


"...a highly interesting book recommendable to anyone interested in evolutionary optimization and to those facing noisy optimization problems." — Hans-Georg Beyer


Contents

Foreword by Hans-Georg Beyer
Acknowledgments

1 Introduction
2 Preliminaries
3 The (1+1)-ES: Overvaluation
4 The (&mu,&lambda)-ES: Distributed Populations
5 The (&mu/&mu,&lambda)-ES: Genetic Repair
6 Comparing Approaches to Noisy Optimization
7 Conclusions

Appendices
References
Index


Other Volumes in the Series

Vol. 1: Efficient and Accurate Parallel Genetic Algorithms
    Erick Cantú-Paz
Vol. 2: Estimation of Distribution Algorithms
    Pedro Larrañaga and José A. Lozano
Vol. 3: Evolutionary Optimization in Dynamic Environments
    Jürgen Branke
Vol. 4: Anticipatory Learning Classifier Systems
    Martin V. Butz
Vol. 5: Evolutionary Algorithms for Solving Multi-Objective Problems
    Carlos A. Coello Coello, David A. Van Veldhuizen and Gary B. Lamont
Vol. 6: OmeGA
    Dimitri Knjazew
Vol. 7: The Design of Innovation
    David E. Goldberg
Vol. 9: Classical and Evolutionary Algorithms in the Optimization of Optical Systems
    Darko Vasiljevic
Vol. 10: Evolutionary Algorithms for Embedded System Design
    Rolf Drechsler and Nicole Drechsler (eds.)

More information about this book series is available here.