A Genetic Algorithm Based on Hybrid Genetic
Operators for Solving Constrained Optimization
>For the case that the optima of constrained optimization problems are often located on boundary of the feasible region, a new genetic algorithm based on hybrid genetic operators is proposed in this paper. First, in this algorithm, the crossover is executed according to feasible and infeasible individuals, respectively. A feasible point is always combined with the best-known one found so far for crossover, whereas an infeasible individual is selected according to the fitness for crossover with any feasible one. In addition, in order to make infeasible solutions become feasible ones and make feasible points move toward the boundary of feasible region, a hybrid mutation operator is presented based on boundary mutation and Gaussian mutation. Numerical experiments and comparison results show the efficiency of the method.
文章引用: 万建妮 , 李和成 (2014) 一种求解约束优化问题基于混合遗传算子的遗传算法。 运筹与模糊学， 4， 1-6. doi: 10.12677/ORF.2014.41001
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