Published:
May 1999
Proceedings:
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference (FLAIRS 1999)
Volume
Issue:
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference (FLAIRS 1999)
Track:
All Papers
Downloads:
Abstract:
Self-adaptation has been frequently employed in evolutionary search. Angeline [1995] defines three distinct levels of self- adaptation which are: population,individual,and component levels. Cultural Algorithms developed by Reynolds[1979] have been shown to provide a framework in which to model self-adaptation at each of these levels.Here, we examine the role that different forms of knowledge can play in the self-adaptation process at the pop- ulation level for evolution-based function optimizers. In particular, we compare the relative performance of normative and situational knowledge in guiding the search process. Properties of functional lanscapes as suggested by Winston [1992] are used to classify the functions associated with 27 different benchmark optimization problems. The results suggest that the type of knowledge used to direct effective search for a given problem depends upon the combination of properties that are found in the associated functional landscape.
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FLAIRS
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference (FLAIRS 1999)
ISBN 978-1-57735-080-4
Published by The AAAI Press, Menlo Park, California.