Tracking Extrema in Dynamic Environments

Abstract

Abstract. Typical applications of evolutionary optimization involve the off-line approximation of extrema of static multi-modal functions. Methods which use a variety of techniques to self-adapt mutation parameters have been shown to be more successful than methods which do not use self-adaptation. For dynamic functions, the interest is not to obtain the extrema but to follow it as closely as possible. This paper compares the on-line extrema tracking performance of an evolutionary program without self-adaptation against an evolutionary program using a self-adaptive Gaussian update rule over a number of dynamics applied to a simple static function. The experiments demonstrate that for some dynamic functions, self-adaptation is effective while for others it is detrimental. Typical applications of evolutionary optimization involve the off-line approximation of extrema of static multi-modal functions. Methods which use a variety of techniques to self-adapt mutation parameters have been shown to be more successful than methods which do not use self-adaptation. For dynamic functions, the interest is not to obtain the extrema but to follow it as closely as possible. This paper compares the on-line extrema tracking performance of an evolutionary program without self-adaptation against an evolutionary program using a self-adaptive Gaussian update rule over a number of dynamics applied to a simple static function. The experiments demonstrate that for some dynamic functions, self-adaptation is effective while for others it is detrimental.

DOI: 10.1007/BFb0014823

Extracted Key Phrases

4 Figures and Tables

Statistics

01020'98'00'02'04'06'08'10'12'14'16
Citations per Year

144 Citations

Semantic Scholar estimates that this publication has 144 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Angeline1997TrackingEI, title={Tracking Extrema in Dynamic Environments}, author={Peter J. Angeline}, booktitle={Evolutionary Programming}, year={1997} }