Behavior study of genetic operators for the minimum sum coloring problem

Evolutionary algorithms are very popular in solving combinatorial optimization problems. Their efficiency is basically related to the appropriate choice of genetic operators, especially the crossover. The performance of this operator depends on the problem definition, the instance structure and the fitness function. The problem of interest in this work is the minimum sum coloring problem (MSCP). In this paper, several genetic operators are studied by varying the instances and the performance measures. Results provide a relevant idea about the effectiveness of the tested operators and show the well suited for the MSCP among them.