Self-adaptation by co-evolution:
Gespeichert in:
Bibliographische Detailangaben
Beteiligte Personen: Tuson, Andrew (VerfasserIn), Ross, Peter (VerfasserIn)
Format: Buch
Sprache:Englisch
Veröffentlicht: Edinburgh 1996
Schriftenreihe:University <Edinburgh> / Department of Artificial Intelligence: DAI research paper 788
Schlagwörter:
Abstract:Abstract: "Traditional genetic algorithms use operator settings such as the crossover rate or number of crossover points that are fixed throughout a given run. The choice of settings can have a major effect on performance, but finding good settings can be hard. One option is to encode the operator settings onto each member of the GA population, and allow them to evolve too. This paper describes an empirical investigation into co-evolving operator settings in genetic algorithms. The results indicate that the problem representation and the choice of operators that are applied to the encoded operator settings is important for useful adaptation to take place."
Umfang:6 S.
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Teilbibliothek Mathematik & Informatik, Berichte

Bestandsangaben von Teilbibliothek Mathematik &amp; Informatik, Berichte
Signatur: 0111 2001 B 6034-788
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