Evolution Strategies in Noisy Environments
Dirk V. Arnold
University of Dortmund,
Computer Science XI
2001. 5. 26
Noise is a factor present in almost all real-world optimization problems.
While it can potentially improve convergence reliability in multimodal
settings by preventing convergence towards merely local optima, it is
generally detrimental to the velocity with which an optimum is approached.
Evolution strategies (ES) form a class of evolutionary optimization
procedures that are believed to be able to cope quite well with noise. A
number of interesting results have been obtained by analytically studying
the performance of ES in very simple fitness environments. By restricting
oneself to such simple environments, it becomes possible to single out the
influence on the ability to deal with noise of different components of the
strategies. In this talk, we review some of these recent results. In
particular, we identify fitness overvaluation as a significant influence
on ES performance, discuss the benefits of working with populations of
individuals rather than with single candidate solutions, and study the
effects of genetic repair in the presence of noise.
This page is maintained by Hee-Ju Chang (firstname.lastname@example.org).
Last update: May 8, 2001