Solving graph coloring problem by fuzzy clustering-based genetic algorithm

Young Seol Lee, Sung Bae Cho

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

4 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 languageEnglish
Title of host publicationSimulated Evolution and Learning - 9th International Conference, SEAL 2012, Proceedings
Pages351-360
Number of pages10
DOIs
Publication statusPublished - 2012
Event9th International Conference on Simulated Evolution and Learning, SEAL 2012 - Hanoi, Viet Nam
Duration: 2012 Dec 162012 Dec 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7673 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Simulated Evolution and Learning, SEAL 2012
CountryViet Nam
CityHanoi
Period12/12/1612/12/19

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • 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