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.

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zlib/Computers/Dirk V. Arnold (auth.)/Noisy Optimization With Evolution Strategies_2119111.pdf

by Dirk V. Arnold

Springer Nature

Genetic Algorithms and Evolutionary Computation, 1568-2587 -- 8, Genetic algorithms and evolutionary computation -- 8., Boston, MA, Massachusetts, 2002

Genetic Algorithms and Evolutionary Computation, 8, 1st ed. 2002, New York, NY, 2002

United States, United States of America

Springer Nature, New York, NY, 2012

Oct 24, 2012

1, 20121206

lg965207

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Source title: Noisy Optimization With Evolution Strategies (Genetic Algorithms and Evolutionary Computation (8))

Front Matter....Pages i-ix
Introduction....Pages 1-6
Preliminaries....Pages 7-20
The (1 + 1)-ES: Overvaluation....Pages 21-36
The (μ, λ)-ES: Distributed Populations....Pages 37-52
The (μ/μ, λ)-ES: Genetic Repair....Pages 53-77
Comparing Approaches to Noisy Optimization....Pages 79-96
Conclusions....Pages 97-102
Back Matter....Pages 103-158

Genetic Algorithms and Evolutionary Computation
Erscheinungsdatum: 24.10.2012

2013-08-01