Problem difficulty for genetic algorithm in combinatorial optimization

Z. Zukhri, Khairuddin Omar

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

Abstract

This paper presents how difficult to handle (Genetic Algorithm) GA with combinatorial approach in clustering problem and an alternative approach is suggested. Clustering problem can be viewed as combinatorial optimization. In this paper, the objects must be clustered are new students. They must be allocated into a few of classes, so that each class contains students with low gap of intelligence. Initially, we apply GA with combinatorial approach. But experiments only provide a small scale case (200 students and 5 classes). Then we try to apply GA with binary chromosome representation and we evaluate it with the same data. We have successfully improved the performance with this approach. This result seems to indicate that GA is not effective to be applied for solving combinatorial optimization problems in general. We suggest that binary representation approach should be used to avoid this difficulty.

Original languageEnglish
Title of host publication2007 5th Student Conference on Research and Development, SCORED
DOIs
Publication statusPublished - 2007
Event2007 5th Student Conference on Research and Development, SCORED - Selangor
Duration: 11 Dec 200712 Dec 2007

Other

Other2007 5th Student Conference on Research and Development, SCORED
CitySelangor
Period11/12/0712/12/07

Fingerprint

student
intelligence
experiment
performance
Genetic algorithm
Combinatorial optimization
Gas
Clustering
Optimization problem
Experiment

Keywords

  • Combinatorial optimization
  • Genetic algorithm
  • Local optima
  • Permutation representation
  • Problem difficulty

ASJC Scopus subject areas

  • Education
  • Management Science and Operations Research

Cite this

Zukhri, Z., & Omar, K. (2007). Problem difficulty for genetic algorithm in combinatorial optimization. In 2007 5th Student Conference on Research and Development, SCORED [4451368] https://doi.org/10.1109/SCORED.2007.4451368

Problem difficulty for genetic algorithm in combinatorial optimization. / Zukhri, Z.; Omar, Khairuddin.

2007 5th Student Conference on Research and Development, SCORED. 2007. 4451368.

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

Zukhri, Z & Omar, K 2007, Problem difficulty for genetic algorithm in combinatorial optimization. in 2007 5th Student Conference on Research and Development, SCORED., 4451368, 2007 5th Student Conference on Research and Development, SCORED, Selangor, 11/12/07. https://doi.org/10.1109/SCORED.2007.4451368
Zukhri Z, Omar K. Problem difficulty for genetic algorithm in combinatorial optimization. In 2007 5th Student Conference on Research and Development, SCORED. 2007. 4451368 https://doi.org/10.1109/SCORED.2007.4451368
Zukhri, Z. ; Omar, Khairuddin. / Problem difficulty for genetic algorithm in combinatorial optimization. 2007 5th Student Conference on Research and Development, SCORED. 2007.
@inproceedings{2479f5e910964a26b8d844b30523196d,
title = "Problem difficulty for genetic algorithm in combinatorial optimization",
abstract = "This paper presents how difficult to handle (Genetic Algorithm) GA with combinatorial approach in clustering problem and an alternative approach is suggested. Clustering problem can be viewed as combinatorial optimization. In this paper, the objects must be clustered are new students. They must be allocated into a few of classes, so that each class contains students with low gap of intelligence. Initially, we apply GA with combinatorial approach. But experiments only provide a small scale case (200 students and 5 classes). Then we try to apply GA with binary chromosome representation and we evaluate it with the same data. We have successfully improved the performance with this approach. This result seems to indicate that GA is not effective to be applied for solving combinatorial optimization problems in general. We suggest that binary representation approach should be used to avoid this difficulty.",
keywords = "Combinatorial optimization, Genetic algorithm, Local optima, Permutation representation, Problem difficulty",
author = "Z. Zukhri and Khairuddin Omar",
year = "2007",
doi = "10.1109/SCORED.2007.4451368",
language = "English",
isbn = "1424414709",
booktitle = "2007 5th Student Conference on Research and Development, SCORED",

}

TY - GEN

T1 - Problem difficulty for genetic algorithm in combinatorial optimization

AU - Zukhri, Z.

AU - Omar, Khairuddin

PY - 2007

Y1 - 2007

N2 - This paper presents how difficult to handle (Genetic Algorithm) GA with combinatorial approach in clustering problem and an alternative approach is suggested. Clustering problem can be viewed as combinatorial optimization. In this paper, the objects must be clustered are new students. They must be allocated into a few of classes, so that each class contains students with low gap of intelligence. Initially, we apply GA with combinatorial approach. But experiments only provide a small scale case (200 students and 5 classes). Then we try to apply GA with binary chromosome representation and we evaluate it with the same data. We have successfully improved the performance with this approach. This result seems to indicate that GA is not effective to be applied for solving combinatorial optimization problems in general. We suggest that binary representation approach should be used to avoid this difficulty.

AB - This paper presents how difficult to handle (Genetic Algorithm) GA with combinatorial approach in clustering problem and an alternative approach is suggested. Clustering problem can be viewed as combinatorial optimization. In this paper, the objects must be clustered are new students. They must be allocated into a few of classes, so that each class contains students with low gap of intelligence. Initially, we apply GA with combinatorial approach. But experiments only provide a small scale case (200 students and 5 classes). Then we try to apply GA with binary chromosome representation and we evaluate it with the same data. We have successfully improved the performance with this approach. This result seems to indicate that GA is not effective to be applied for solving combinatorial optimization problems in general. We suggest that binary representation approach should be used to avoid this difficulty.

KW - Combinatorial optimization

KW - Genetic algorithm

KW - Local optima

KW - Permutation representation

KW - Problem difficulty

UR - http://www.scopus.com/inward/record.url?scp=50449090110&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=50449090110&partnerID=8YFLogxK

U2 - 10.1109/SCORED.2007.4451368

DO - 10.1109/SCORED.2007.4451368

M3 - Conference contribution

SN - 1424414709

SN - 9781424414703

BT - 2007 5th Student Conference on Research and Development, SCORED

ER -