| On 2004-01-13 at 15:00:00 (Brussels Time) |
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
