Partially evaluated genetic algorithm based on fuzzy c-Means algorithm

Si Ho Yoo, Sung Bae Cho

Research output: Contribution to journalArticle

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
Pages (from-to)440-449
Number of pages10
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3242
Publication statusPublished - 2004 Dec 1

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Fuzzy C-means Algorithm
Fitness
Genetic algorithms
Genetic Algorithm
Cluster Analysis
Fuzzy clustering
Clustering algorithms
Costs
Costs and Cost Analysis
Benchmarking
Population Density
Pearson Correlation
Fuzzy Clustering
Euclidean Distance
Population Size
Correlation coefficient
Clustering Algorithm
Optimal Solution
Benchmark
Population

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

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