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Speeding Up Evolution
Jens Ziegler
University of Dortmund
Dept. of Computer Science
Systems Analysis
jens.ziegler@uni-dortmund.de

Abstract

The high number of fitness evaluations in evolutionary algorithms is often expensive, time-consuming or otherwise problematic in many real-world applications. Reducing the number and duration of evaluations is thus a desirable goal in order to reduce the overall runtime of the evolutionary process. It can be shown that the result of a classifier, trained to discriminate between good and worse individuals, can be used in combination with tournament selection to both yield better results with less evaluations.

Keywords

Evolutionary Algorithms, Classification, Meta-Evolution