Novel constrained optimization genetic algorithm and its engineering applications
一种新的约束优化遗传算法及其工程应用

This paper proposed a novel genetic algorithm to solve constrained optimization problems. It introduced the indivi-dual generation based on good-point-set method into the genetic algorithm initial step, which maintained the population diversity of the genetic algorithm. In the evolution process, it searched the decision space of a problem through the arithmetic crossover operator of feasible and infeasible solutions. In order to coordinate the exploitation and the exploration ability of the algorithm, it used Gaussian and Cauchy mutation operators to the feasible and infeasible subpopulation respectively. It tested several benchmark problems and two engineering design problems. The results show that the proposed method is an effective way for constrained optimization problems.