Partially evaluated genetic algorithm based on fuzzy c-Means algorithm

Si Ho Yoo, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingChapter

10 Citations (Scopus)

Abstract

To find the optimal solution with genetic algorithm, it is desirable to maintain the population size 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 partially evaluated GA based on fuzzy clustering, which considerably reduces evaluation cost without any 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 according to membership matrix. The results with nine benchmark functions are compared to six hard clustering algorithms with Euclidean distance and Pearson correlation coefficients for measuring the similarity between the representative and its members in fitness distribution.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsXin Yao, John A. Bullinaria, Jonathan Rowe, Peter Tino, Ata Kaban, Edmund Burke, Jose A. Lozano, Jim Smith, Juan J. Merelo-Guervos, Hans-Paul Schwefel
PublisherSpringer Verlag
Pages440-449
Number of pages10
ISBN (Print)3540230920, 9783540230922
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3242
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Yoo, S. H., & Cho, S. B. (2004). Partially evaluated genetic algorithm based on fuzzy c-Means algorithm. In X. Yao, J. A. Bullinaria, J. Rowe, P. Tino, A. Kaban, E. Burke, J. A. Lozano, J. Smith, J. J. Merelo-Guervos, & H-P. Schwefel (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 440-449). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3242). Springer Verlag. https://doi.org/10.1007/978-3-540-30217-9_45