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Proceedings Paper

Case studies in applying fitness distributions in evolutionary algorithms: I. Simple neural networks and Gaussion mutation
Author(s): Ankit Jain; David B. Fogel
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Paper Abstract

Evolutionary algorithms are often applied to tasks where the challenges is to find a superior solution. The engineering challenge concerns how to best design such algorithms in terms of their representation, variation operators, and selection. The distribution of fitness scores that is obtained when applying variation operators to parents can provide useful information for setting the parameters that are associated with those operators. Experiments presented here indicate that fitness distributions can also reveal information about the landscapes that surrounds particular parents and suggest that typical methods of self-adaption may not be very well suited for exploring the state space of possible solutions in the presence of multiple minima.

Paper Details

Date Published: 30 March 2000
PDF: 8 pages
Proc. SPIE 4055, Applications and Science of Computational Intelligence III, (30 March 2000); doi: 10.1117/12.380569
Show Author Affiliations
Ankit Jain, Netaji Subhas Institute of Technology (India)
David B. Fogel, Natural Selection, Inc. (United States)


Published in SPIE Proceedings Vol. 4055:
Applications and Science of Computational Intelligence III
Kevin L. Priddy; Paul E. Keller; David B. Fogel, Editor(s)

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