Evolutionary algorithm using feasibility-based grouping for numerical constrained optimization problems

Different strategies for defining the relationship between feasible and infeasible individuals in evolutionary algorithms can provide with very different results when solving numerical constrained optimization problems. This paper proposes a novel EA to balance the relationship between feasible and infeasible individuals to solve numerical constrained optimization problems. According to the feasibility of the individuals, the population is divided into two groups, feasible group and infeasible group. The evaluation and ranking of these two groups are performed separately. Parents for reproduction are selected from the two groups by a novel parent selection method. The proposed method is tested using (μ, λ) evolution strategies with 13 benchmark problems. The results show that the proposed method improves the searching performance for most of the tested problems.