Fitness approximation for genetic algorithm using combination of approximation model and fuzzy clustering technique

Jong Won Yoon; Sung Bae Cho

doi:10.1109/CEC.2010.5586519

Fitness approximation for genetic algorithm using combination of approximation model and fuzzy clustering technique

Jong Won Yoon, Sung Bae Cho

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

A genetic algorithm can be applied to various search or optimization problems. However, there exists a problem that it takes too much cost to evaluate a large number of individuals. To deal with the problem, the fitness approximation method which reduces the cost of the evaluation with the similar performance to the general GA is needed. We proposed the fitness approximation using a combination of the approximation model and the fuzzy clustering technique. There exist two advantages of the proposed method. First, it reduces the cost of the fitness evaluation. Second, it shows the similar performance to the general GA. To verify the performance of the method, we designed the experiments using several benchmark functions and compared other fitness approximation methods.

Original language	English
Title of host publication	2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
DOIs	https://doi.org/10.1109/CEC.2010.5586519
Publication status	Published - 2010
Event	2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 - Barcelona, Spain Duration: 2010 Jul 18 → 2010 Jul 23

Publication series

Name	2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

Other

Other	2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
Country/Territory	Spain
City	Barcelona
Period	10/7/18 → 10/7/23

All Science Journal Classification (ASJC) codes

Computational Theory and Mathematics
Applied Mathematics

Access to Document

10.1109/CEC.2010.5586519

Cite this

Yoon, J. W., & Cho, S. B. (2010). Fitness approximation for genetic algorithm using combination of approximation model and fuzzy clustering technique. In 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 Article 5586519 (2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010). https://doi.org/10.1109/CEC.2010.5586519

Yoon, Jong Won ; Cho, Sung Bae. / Fitness approximation for genetic algorithm using combination of approximation model and fuzzy clustering technique. 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010. 2010. (2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010).

@inproceedings{afaa97cd0fd345ec99315dfab6a98eba,

title = "Fitness approximation for genetic algorithm using combination of approximation model and fuzzy clustering technique",

abstract = "A genetic algorithm can be applied to various search or optimization problems. However, there exists a problem that it takes too much cost to evaluate a large number of individuals. To deal with the problem, the fitness approximation method which reduces the cost of the evaluation with the similar performance to the general GA is needed. We proposed the fitness approximation using a combination of the approximation model and the fuzzy clustering technique. There exist two advantages of the proposed method. First, it reduces the cost of the fitness evaluation. Second, it shows the similar performance to the general GA. To verify the performance of the method, we designed the experiments using several benchmark functions and compared other fitness approximation methods.",

author = "Yoon, {Jong Won} and Cho, {Sung Bae}",

year = "2010",

doi = "10.1109/CEC.2010.5586519",

language = "English",

isbn = "9781424469109",

series = "2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010",

booktitle = "2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010",

note = "2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 ; Conference date: 18-07-2010 Through 23-07-2010",

}

Yoon, JW & Cho, SB 2010, Fitness approximation for genetic algorithm using combination of approximation model and fuzzy clustering technique. in 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010., 5586519, 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010, Barcelona, Spain, 10/7/18. https://doi.org/10.1109/CEC.2010.5586519

Fitness approximation for genetic algorithm using combination of approximation model and fuzzy clustering technique. / Yoon, Jong Won; Cho, Sung Bae.
2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010. 2010. 5586519 (2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Yoon, Jong Won

AU - Cho, Sung Bae

PY - 2010

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N2 - A genetic algorithm can be applied to various search or optimization problems. However, there exists a problem that it takes too much cost to evaluate a large number of individuals. To deal with the problem, the fitness approximation method which reduces the cost of the evaluation with the similar performance to the general GA is needed. We proposed the fitness approximation using a combination of the approximation model and the fuzzy clustering technique. There exist two advantages of the proposed method. First, it reduces the cost of the fitness evaluation. Second, it shows the similar performance to the general GA. To verify the performance of the method, we designed the experiments using several benchmark functions and compared other fitness approximation methods.

AB - A genetic algorithm can be applied to various search or optimization problems. However, there exists a problem that it takes too much cost to evaluate a large number of individuals. To deal with the problem, the fitness approximation method which reduces the cost of the evaluation with the similar performance to the general GA is needed. We proposed the fitness approximation using a combination of the approximation model and the fuzzy clustering technique. There exist two advantages of the proposed method. First, it reduces the cost of the fitness evaluation. Second, it shows the similar performance to the general GA. To verify the performance of the method, we designed the experiments using several benchmark functions and compared other fitness approximation methods.

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T3 - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

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Yoon JW, Cho SB. Fitness approximation for genetic algorithm using combination of approximation model and fuzzy clustering technique. In 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010. 2010. 5586519. (2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010). doi: 10.1109/CEC.2010.5586519

Fitness approximation for genetic algorithm using combination of approximation model and fuzzy clustering technique

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