A Hybrid Immune Algorithm with Information Gain for the Graph Coloring Problem

We present a new Immune Algorithm that incorporates a simple local search procedure to improve the overall performances to tackle the graph coloring problem instances. We characterize the algorithm and set its parameters in terms of Information Gain. Experiments will show that the IA we propose is very competitive with the best evolutionary algorithms.

[1]  Chak-Kuen Wong,et al.  A new model of simulated evolutionary computation-convergence analysis and specifications , 2001, IEEE Trans. Evol. Comput..

[2]  S. Motta,et al.  Pattern recognition by primary and secondary response of an Artificial Immune System , 2001 .

[3]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[4]  Jin-Kao Hao,et al.  Hybrid Evolutionary Algorithms for Graph Coloring , 1999, J. Comb. Optim..

[5]  M. Trick,et al.  Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, Workshop, October 11-13, 1993 , 1996 .

[6]  Leandro Nunes de Castro,et al.  The Clonal Selection Algorithm with Engineering Applications 1 , 2000 .

[7]  Michael A. Trick,et al.  A Column Generation Approach for Graph Coloring , 1996, INFORMS J. Comput..

[8]  Giuseppe Nicosia,et al.  Pattern recognition by primary and secondary response of an Artificial Immune System , 2001, Theory in Biosciences.

[9]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[10]  Robert I. Damper,et al.  Breaking the symmetry of the graph colouring problem with genetic algorithms , 2000 .

[11]  Paolo Dell'Olmo,et al.  Iterative coloring extension of a maximum clique , 2001 .

[12]  S. Forrest,et al.  Immunology as Information Processing , 2001 .

[13]  Jonathan Timmis,et al.  Artificial immune systems - a new computational intelligence paradigm , 2002 .

[14]  Valmir Carneiro Barbosa,et al.  Two Novel Evolutionary Formulations of the Graph Coloring Problem , 2003, J. Comb. Optim..

[15]  D. Dasgupta Artificial Immune Systems and Their Applications , 1998, Springer Berlin Heidelberg.

[16]  P E Seiden,et al.  A model for simulating cognate recognition and response in the immune system. , 1992, Journal of theoretical biology.

[17]  Cecilia R. Aragon,et al.  Optimization by Simulated Annealing: An Experimental Evaluation; Part II, Graph Coloring and Number Partitioning , 1991, Oper. Res..