Designing competent mutation operators via probabilistic model building of neighborhoods (2004)
| Venue: | In Deb, K., & et al. (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004), Part II, LNCS 3103 |
| Citations: | 30 - 21 self |
BibTeX
@TECHREPORT{Sastry04designingcompetent,
author = {Kumara Sastry and Kumara Sastry and David E. Goldberg and David E. Goldberg},
title = {Designing competent mutation operators via probabilistic model building of neighborhoods},
institution = {In Deb, K., & et al. (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004), Part II, LNCS 3103},
year = {2004}
}
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Abstract
This paper presents a competent selectomutative genetic algorithm (GA), that adapts linkage and solves hard problems quickly, reliably, and accurately. A probabilistic model building process is used to automatically identify key building blocks (BBs) of the search problem. The mutation operator uses the probabilistic model of linkage groups to find the best among competing building blocks. The competent selectomutative GA successfully solves additively separable problems of bounded difficulty, requiring only subquadratic number of function evaluations. The results show that for additively separable problems the probabilistic model building BBwise mutation scales as O(2 k m 1.5), and requires O ( √ k log m) less function evaluations than its selectorecombinative counterpart, confirming theoretical results reported elsewhere (Sastry & Goldberg, 2004). 1
