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

Jong Won Yoon, Sung-Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (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 languageEnglish
Title of host publication2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
DOIs
Publication statusPublished - 2010 Dec 1
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 - Barcelona, Spain
Duration: 2010 Jul 182010 Jul 23

Publication series

Name2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

Other

Other2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
CountrySpain
CityBarcelona
Period10/7/1810/7/23

Fingerprint

Fuzzy clustering
Fuzzy Clustering
Fitness
Genetic algorithms
Genetic Algorithm
Approximation
Approximation Methods
Costs
Search Problems
Evaluation
Model
Benchmark
Verify
Optimization Problem
Evaluate
Experiments
Experiment
Gas

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Applied Mathematics

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 [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 Sung-Bae Cho",
year = "2010",
month = "12",
day = "1",
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",

}

Yoon, JW & 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., 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 proceedingConference contribution

TY - GEN

T1 - Fitness approximation for genetic algorithm using combination of approximation model and fuzzy clustering technique

AU - Yoon, Jong Won

AU - Cho, Sung-Bae

PY - 2010/12/1

Y1 - 2010/12/1

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.

UR - http://www.scopus.com/inward/record.url?scp=79959415155&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79959415155&partnerID=8YFLogxK

U2 - 10.1109/CEC.2010.5586519

DO - 10.1109/CEC.2010.5586519

M3 - Conference contribution

AN - SCOPUS:79959415155

SN - 9781424469109

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

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

ER -

Yoon JW, Cho S-B. 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). https://doi.org/10.1109/CEC.2010.5586519