Robustness of Populations in Stochastic Environments

被引:45
|
作者
Giessen, Christian [1 ]
Koetzing, Timo [2 ]
机构
[1] Tech Univ Denmark, DK-2800 Lyngby, Denmark
[2] Univ Jena, D-07743 Jena, Germany
关键词
Run time analysis; Stochastic fitness function; Evolutionary algorithm; Populations; Robustness; EVOLUTIONARY; OPTIMIZATION; ALGORITHMS; CHOICE; SIZE;
D O I
10.1007/s00453-015-0072-0
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
We consider stochastic versions of ONEMAX and LEADINGONES and analyze the performance of evolutionary algorithms with and without populations on these problems. It is known that the (1+ 1) EA on ONEMAX performs well in the presence of very small noise, but poorly for higher noise levels. We extend these results to LEADINGONES and to many different noise models, showing howthe application of drift theory can significantly simplify and generalize previous analyses. Most surprisingly, even small populations (of size Theta (log n)) can make evolutionary algorithms perform well for high noise levels, well outside the abilities of the (1 + 1) EA. Larger population sizes are even more beneficial; we consider both parent and offspring populations. In this sense, populations are robust in these stochastic settings.
引用
收藏
页码:462 / 489
页数:28
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