自适应遗传算法交叉变异算子的改进

标准遗传算法采用固定的交叉率和变异率,对于求解一般的全局最优问题具有较好的鲁棒性,而对于解决较复杂的优化问题则存在早熟及稳定性差的缺点。传统的自适应遗传算法虽能有效提高算法的收敛速度,却难以提高优良解的多样性,算法的鲁棒性仍有待改善。文章提出了一种改进的自适应遗传算法,对交叉算子和变异算子进行了优化,实现了交叉率和变异率的非线性自适应调整。实验结果表明,相比传统的自适应遗传算法,新算法具有更快的收敛速度和更可靠的稳定性。

Improving Crossover and Mutation for Adaptive Genetic Algorithm

The Standard Genetic Algorithm(SGA) adopts constant crossover probability as well as invariable mutationprobability.It has such disadvantages as premature convergence,low convergence speed and low robustness.Common adaptation of parameters and operators for SGA is hard to obtain high-quality solution,though it promotes the convergence speed.This paper presents a method for optimal design of an improved adaptive Genetic Algorithm making the crossover probability and mutation probability adjust adaptively and nonlinearly.The case study of designing and simulation shows our new method has faster convergence speed and higher robustness.