A clustering entropy-driven approach for exploring and exploiting noisy functions

Authors:
Shih-Hsi Liu;Marjan Mernik;Barrett R. Bryant
Affiliations:
Univ. of Alabama at Birmingham, Birmingham, AL;Univ. of Maribor, Maribor, Slovenia;Univ. of Alabama at Birmingham, Birmingham, AL
Venue:
Proceedings of the 2007 ACM symposium on Applied computing
Year:
2007

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Abstract

Linear, Gaussian, fitness proportional, clustering, and Rosca entropies are succinct measures of diversity that have been applied to balance exploration and exploitation in evolutionary algorithms. In previous studies, an entropy-driven approach using linear entropy explicitly balances and/or searches optimal solutions for the selected unimodal and multimodal functions excluding noisy functions. This paper investigates the reasons for such an exception and introduces a clustering entropy-driven approach to solve the problem. Such an approach provides a coarse-grained diversity measure that filters the noise of functions, varies cluster size and categorizes individuals at the genotype level. The experimental results show that the clustering entropy-driven approach further improves the searching results of noisy functions by one more degree.