Noisy Optimization With Evolution Strategies

1. Auflage 2002
ISBN: 978-1-4020-7105-8
Verlag: Springer Netherlands

Buch, Englisch, Band 8, 158 Seiten, GB, Format (B × H): 155 mm x 235 mm, Gewicht: 421 g

Reihe: Genetic Algorithms and Evolutionary Computation

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.

Zielgruppe

Research

Autoren/Hrsg.

Arnold, Dirk V.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik Mathematik Operations Research Spieltheorie
• Mathematik | Informatik EDV | Informatik Informatik Logik, formale Sprachen, Automaten
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik

Inhaltsverzeichnis

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.