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Proceedings Paper

Fitness distributions in evolutionary computation: analysis of noisy functions
Author(s): Kumar Chellapilla; David B. Fogel
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Paper Abstract

Traditional techniques for designing evolutionary algorithms rely on schema processing, minimizing expected losses, and emphasize certain genetic operators such as crossover. Unfortunately, these have failed to provide robust optimization performance. Recently, fitness distribution analysis has been proposed as an alternative tool for designing efficient evolutionary computations. This analysis has concentrated on obtaining very accurate expected improvement (EI) and probability of improvement (PI) statistics for specific mutation operators (using as many as 5000 Monte Carlo trials) on noiseless object functions. In practice, such extensive analysis might be computationally prohibitive and the objective functions might also be noisy. Experiments were designed here to determine the amount of sampling required to obtain useful estimates of the EI and PI both in the presence and absence of noise. Simulations indicate that useful statistics can be obtained in as few as 10 trials in the absence of noise. On noisy functions, however, the required number of trials increased as the 'signal to noise ratio' decreased.

Paper Details

Date Published: 22 March 1999
PDF: 11 pages
Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999); doi: 10.1117/12.342886
Show Author Affiliations
Kumar Chellapilla, Univ. of California/San Diego (United States)
David B. Fogel, Natural Selection, Inc. (United States)


Published in SPIE Proceedings Vol. 3722:
Applications and Science of Computational Intelligence II
Kevin L. Priddy; Paul E. Keller; David B. Fogel; James C. Bezdek, Editor(s)

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