An Efficient GA with Multipoint Guided Mutation for Graph Coloring Problems

Proper coloring of the vertices of a graph with minimum number of colors has always been of great interest of researchers in the field of soft computing. Genetic Algorithm (GA) and its application as the solution method to the Graph Coloring problem have been appreciated and worked upon by the scientists almost for the last two decades. Various genetic operators such as crossover and mutation have been used in the GA probabilistically in the previous works, which distributes the promising solutions in the search space at each generation. This paper introduces a new operator, called double point Guided Mutation operator with a special feature. An evolutionary algorithm with double point Guided Mutation for the Graph Coloring problem is proposed here, which could advance the performance level of simple GA dramatically. The algorithm has been tested upon a large-scale test graphs and has shown better output than the earlier works on the same problem. This paper describes the advancement of performance of simple GA applied upon the problem of graph coloring using a operator called double point Guided Mutation in association of the general genetic operators Crossover and Mutation used probabilistically. Our work is still going on for designing better algorithms.