### 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 language | English |
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Pages (from-to) | 440-449 |

Number of pages | 10 |

Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Volume | 3242 |

Publication status | Published - 2004 Dec 1 |

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### All Science Journal Classification (ASJC) codes

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

### Cite this

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**Partially evaluated genetic algorithm based on fuzzy c-Means algorithm.** / Yoo, Si Ho; Cho, Sung Bae.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Partially evaluated genetic algorithm based on fuzzy c-Means algorithm

AU - Yoo, Si Ho

AU - Cho, Sung Bae

PY - 2004/12/1

Y1 - 2004/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=26944456476&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=26944456476&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:26944456476

VL - 3242

SP - 440

EP - 449

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -