Welcome to the InfoSci Platform
Reference Hub6
Stochastic Learning for SAT- Encoded Graph Coloring Problems

Stochastic Learning for SAT- Encoded Graph Coloring Problems

Noureddine Bouhmala, Ole-Christoffer Granmo
Copyright: © 2010 |Volume: 1 |Issue: 3 |Pages: 19
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781609609542|DOI: 10.4018/jamc.2010070101
Cite Article Cite Article

MLA

Bouhmala, Noureddine, and Ole-Christoffer Granmo. "Stochastic Learning for SAT- Encoded Graph Coloring Problems." IJAMC vol.1, no.3 2010: pp.1-19. http://doi.org/10.4018/jamc.2010070101

APA

Bouhmala, N. & Granmo, O. (2010). Stochastic Learning for SAT- Encoded Graph Coloring Problems. International Journal of Applied Metaheuristic Computing (IJAMC), 1(3), 1-19. http://doi.org/10.4018/jamc.2010070101

Chicago

Bouhmala, Noureddine, and Ole-Christoffer Granmo. "Stochastic Learning for SAT- Encoded Graph Coloring Problems," International Journal of Applied Metaheuristic Computing (IJAMC) 1, no.3: 1-19. http://doi.org/10.4018/jamc.2010070101

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

The graph coloring problem (GCP) is a widely studied combinatorial optimization problem due to its numerous applications in many areas, including time tabling, frequency assignment, and register allocation. The need for more efficient algorithms has led to the development of several GC solvers. In this paper, the authors introduce a team of Finite Learning Automata, combined with the random walk algorithm, using Boolean satisfiability encoding for the GCP. The authors present an experimental analysis of the new algorithm’s performance compared to the random walk technique, using a benchmark set containing SAT-encoding graph coloring test sets.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.