一种求解约束优化问题的遗传算法

提出一种求解约束优化问题的遗传算法。通过可行解与不可行解算术交叉的方法对问题的决策空间进行搜索，对可行种群和不可行种群分别按照适应度和约束违反度进行选择。传统变异操作使得解往往偏离了约束区域，因此引入对可行解的边界变异和对不可行解的非均匀变异，并通过维变异方法保持种群的多样性。数值实验结果说明该算法的有效性。

Genetic Algorithm for Solving Constrained Optimization Problem

A genetic algorithm to handle constrained optimization problem is proposed. This method searches the decision space of a problem through the arithmetic crossover of feasible and infeasible solutions, and performs a selection on feasible and infeasible populations respectively according to fitness and constraint violation. It uses the boundary mutation on feasible solutions and the non-uniform mutation on infeasible solutions because the solutions usually deviate from the constraint domain after the traditional mutation operation. It maintains the population diversity through dimension mutation. Numerical results show that it is an effective algorithm.