Grouping-based evolutionary algorithm: seeking balance between feasible and infeasible individuals of constrained optimization problems

Most of the optimization problems in the real world have constraints. In recent years, evolutionary algorithms caught a lot of researchers' attention for solving constrained optimization problems. Infeasible individuals are often underrated by most of the current evolutionary algorithms when evolutionary algorithms are used for solving constraint optimization problems. This paper proposes an approach to balance the feasible and infeasible individuals. Feasible and infeasible individuals are 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 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 algorithm: stochastic ranking method, is modified to evaluate and rank the infeasible group. The new method is tested using a (/spl mu/, /spl lambda/)-ES on 13 benchmark problems. The results show that the proposed method is capable of improving the searching performance of the stochastic ranking method.