Solving graph coloring problem by fuzzy clustering-based genetic algorithm

Young Seol Lee; Sung Bae Cho

doi:10.1007/978-3-642-34859-4_35

Solving graph coloring problem by fuzzy clustering-based genetic algorithm

Young Seol Lee, Sung Bae Cho

Department of Computer Science

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

3 Citations (Scopus)

Abstract

The graph coloring problem is one of famous combinatorial optimization problems. Some researchers attempted to solve combinatorial optimization problem with evolutionary algorithm, which can find near optimal solution based on the evolution mechanism of the nature. However, it sometimes requires too much cost to evaluate fitness of a large number of individuals in the population when applying the GA to the real world problems. This paper attempts to solve graph coloring problem using a fuzzy clustering based evolutionary approach to reduce the cost of the evaluation. In order to show the feasibility of the method, some experiments with other alternative methods are conducted.

Original language	English
Title of host publication	Simulated Evolution and Learning - 9th International Conference, SEAL 2012, Proceedings
Pages	351-360
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-34859-4_35
Publication status	Published - 2012 Dec 26
Event	9th International Conference on Simulated Evolution and Learning, SEAL 2012 - Hanoi, Viet Nam Duration: 2012 Dec 16 → 2012 Dec 19

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7673 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	9th International Conference on Simulated Evolution and Learning, SEAL 2012
Country	Viet Nam
City	Hanoi
Period	12/12/16 → 12/12/19

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All Science Journal Classification (ASJC) codes

Computer Science(all)
Theoretical Computer Science

Cite this

Lee, Y. S., & Cho, S. B. (2012). Solving graph coloring problem by fuzzy clustering-based genetic algorithm. In Simulated Evolution and Learning - 9th International Conference, SEAL 2012, Proceedings (pp. 351-360). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7673 LNCS). https://doi.org/10.1007/978-3-642-34859-4_35

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Lee, YS & Cho, SB 2012, Solving graph coloring problem by fuzzy clustering-based genetic algorithm. in Simulated Evolution and Learning - 9th International Conference, SEAL 2012, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7673 LNCS, pp. 351-360, 9th International Conference on Simulated Evolution and Learning, SEAL 2012, Hanoi, Viet Nam, 12/12/16. https://doi.org/10.1007/978-3-642-34859-4_35

Solving graph coloring problem by fuzzy clustering-based genetic algorithm. / Lee, Young Seol; Cho, Sung Bae.

Simulated Evolution and Learning - 9th International Conference, SEAL 2012, Proceedings. 2012. p. 351-360 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7673 LNCS).

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

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AB - The graph coloring problem is one of famous combinatorial optimization problems. Some researchers attempted to solve combinatorial optimization problem with evolutionary algorithm, which can find near optimal solution based on the evolution mechanism of the nature. However, it sometimes requires too much cost to evaluate fitness of a large number of individuals in the population when applying the GA to the real world problems. This paper attempts to solve graph coloring problem using a fuzzy clustering based evolutionary approach to reduce the cost of the evaluation. In order to show the feasibility of the method, some experiments with other alternative methods are conducted.

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Lee YS, Cho SB. Solving graph coloring problem by fuzzy clustering-based genetic algorithm. In Simulated Evolution and Learning - 9th International Conference, SEAL 2012, Proceedings. 2012. p. 351-360. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-34859-4_35

Access to Document

10.1007/978-3-642-34859-4_35