A Hybrid Evolution Genetic Algorithm for Constrained Optimization

A new hybrid evolution genetic algorithm for constrained optimization is proposed in this paper. This algorithm is based on feasible and infeasible population and mixed crossover with mutation operations. It introduces temporary feasible and infeasible population and maintains a fixed scale of the feasible and infeasible population in each generation. Through the genetic repair strategy and definitions of the different evaluation functions for feasible and infeasible individuals, the diversity of the offspring population and the constringency of the algorithm are ensured. Finally, the numerical examples are used to demonstrate the efficiency of the algorithm.