Evolution of hyperheuristics for the biobjective graph coloring problem using multiobjective genetic programming

We consider a formulation of the biobjective soft graph coloring problem so as to simultaneously minimize the number of colors used as well as the number of edges that connect vertices of the same color. We aim to evolve hyperheuristics for this class of problem using multiobjective genetic programming (MOGP). The major advantage being that these hyperheuristics can then be applied to any instance of this problem. We test the hyperheuristics on benchmark graph coloring problems, and in the absence of an actual Pareto-front, we compare the solutions obtained with existing heuristics. We then further improve the quality of hyperheuristics evolved, and try to make them closer to human-designed heuristics.