Learning probability distributions in continuous evolutionary algorithms - A comparative review

被引：170

作者：

Kern S. ^{[1
]}

Müller S.D. ^{[1
]}

Hansen N. ^{[1
]}

Büche D. ^{[1
]}

Ocenasek J. ^{[2
]}

Koumoutsakos P. ^{[1
,2
]}

机构：

[1] Institute of Computational Science, Swiss Federal Institute of Technol.

[2] Computational Laboratory, Swiss Federal Institute of Technol.

来源：

Natural Computing | 2004年 / 3卷 / 1期

关键词：

Adaptation; Bayesian optimization; Estimation of distribution algorithm; Evolution strategy; Evolutionary algorithm; learning; probability distribution;

D O I：

10.1023/B:NACO.0000023416.59689.4e

中图分类号：

学科分类号：

摘要：

We present a comparative review of Evolutionary Algorithms that generate new population members by sampling a probability distribution constructed during the optimization process. We present a unifying formulation for five such algorithms that enables us to characterize them based on the parametrization of the probability distribution, the learning methodology, and the use of historical information. The algorithms are evaluated on a number of test functions in order to assess their relative strengths and weaknesses. This comparative review helps to identify areas of applicability for the algorithms and to guide future algorithmic developments. © 2004 Kluwer Academic Publishers.

引用

页码：77 / 112

页数：35

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