Noisy Optimization with Evolution Strategies (Genetic Algorithms and Evolutionary Computation Series) / Edition 1

Noisy Optimization with Evolution Strategies (Genetic Algorithms and Evolutionary Computation Series) / Edition 1

by Dirk V. Arnold, Hans-Georg Beyer
     
 

ISBN-10: 1402071051

ISBN-13: 9781402071058

Pub. Date: 06/28/2002

Publisher: Springer-Verlag New York, LLC

Noise is a common factor in most real-world optimization problems. Sources of noise can include physical measurement limitations, shastic 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

Overview

Noise is a common factor in most real-world optimization problems. Sources of noise can include physical measurement limitations, shastic 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.

Product Details

ISBN-13:
9781402071058
Publisher:
Springer-Verlag New York, LLC
Publication date:
06/28/2002
Series:
Genetic Algorithms and Evolutionary Computation Series, #8
Edition description:
2002
Pages:
172
Product dimensions:
9.21(w) x 6.14(h) x 0.44(d)

Table of Contents

Foreword. 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.
A. Some Statistical Basics.
B. Some Useful Identities.
C. Computing the Overvaluation.
D. Determining the Effects of Sampling and Selection.
References. Index.

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