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Partially Evaluated Genetic Algorithm based on Fuzzy Clustering  

Yoo Si-Ho (연세대학교 컴퓨터과학과)
Cho Sung-Bae (연세대학교 컴퓨터과학과)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.9, 2004 , pp. 1246-1257 More about this Journal
Abstract
To find an optimal solution with genetic algorithm, it is desirable to maintain the population sire as large as possible. In some cases, however, the cost to evaluate each individual is relatively high and it is difficult to maintain large population. To solve this problem we propose a novel genetic algorithm based on fuzzy clustering, which considerably reduces evaluation number without any significant loss of its performance by evaluating only one representative for each cluster. The fitness values of other individuals are estimated from the representative fitness values indirectly. We have used fuzzy c-means algorithm and distributed the fitness using membership matrix, since it is hard to distribute precise fitness values by hard clustering method to individuals which belong to multiple groups. Nine benchmark functions have been investigated and the results are compared to six hard clustering algorithms with Euclidean distance and Pearson correlation coefficients as fitness distribution method.
Keywords
genetic algorithm; partially evaluated genetic algorithm; fitness distribution; hard clustering; fuzzy c-means algorithm;
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