Grouping-based Evolutionary Algorithm Improves the Performance of Dynamic Penalty Method for Constrained Optimization Problems

Infeasible individuals are often underrated when evolutionary algorithms are used for solving constraint optimization problems. This paper proposes a new approach to balance the feasible and infeasible 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. Objective function and bubble sort method are selected as the fitness function and ranking method for the feasible group. One existing evolutionary algorithms, dynamic penalty method, is modified to evaluate and rank the infeasible group. The new method is tested using a (μ, λ)-ES on 13 benchmark problems. Our results show that the proposed method is capable of improving performance of the dynamic penalty method for constrained optimization problems.