A grouping-based evolutionary algorithm for constrained optimization problem

Most of the existing evolutionary algorithms for constrained problems derate the importance of the infeasible individuals. In these algorithms, feasible individuals might get more possibility to survive and reproduce than infeasible individuals. To recover the utility of infeasible individuals, a grouping-based evolutionary algorithm (GEA) for constrained problems is proposed in this paper. Feasible population and infeasible individuals are separated as two groups. Evaluation, rank and reproduction of these groups are performed separately. The only chance for the two groups to exchange information happens when the offspring replace the parents. Thus, the designer could pay more attention to the evolutionary process inside the group. The simulation results of four benchmark problems show the effectiveness of the proposed algorithm.