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Noisy optimization with evolution strategies
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| Venue: | SIAM Journal on Optimization |
| Citations: | 39 - 6 self |
BibTeX
@ARTICLE{Arnold_noisyoptimization,
author = {Dirk V. Arnold and Hans-georg Beyer},
title = {Noisy optimization with evolution strategies},
journal = {SIAM Journal on Optimization},
year = {}
}
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Abstract
Evolution strategies are general, nature-inspired heuristics for search and optimization. Supported both by empirical evidence and by recent theoretical findings, there is a common belief that evolution strategies are robust and reliable, and frequently they are the method of choice if neither derivatives of the objective function are at hand nor differentiability and numerical accuracy can be assumed. However, despite their widespread use, there is little exchange between members of the “classical ” optimization community and people working in the field of evolutionary computation. It is our belief that both sides would benefit from such an exchange. In this paper, we present a brief outline of evolution strategies and discuss some of their properties in the presence of noise. We then empirically demonstrate that for a simple but nonetheless nontrivial noisy objective function, an evolution strategy outperforms other optimization algorithms designed to be able to cope with noise. The environment in which the algorithms are tested is deliberately chosen to afford a transparency of the results that reveals the strengths and shortcomings of the strategies, making it possible to draw conclusions with regard to the design of better optimization algorithms for noisy environments. 1
Keyphrases
evolution strategy noisy optimization optimization algorithm recent theoretical finding little exchange nonetheless nontrivial noisy objective function common belief brief outline noisy environment classical optimization community empirical evidence evolutionary computation numerical accuracy nature-inspired heuristic objective function widespread use
