一种双变异率的改进遗传算法及其仿真研究

针对标准遗传算法收敛速度慢，寻优能力差，易陷入局部最优等问题，提出了一种双变异率的改进遗传算法。在进化过程中，引入广义海明距离这个概念，当由广义海明距离控制的交叉操作产生个体数不足种群规模时，对原种群进行局部小变异，这样在避免近亲繁殖的同时又可扩大搜索空间，增加种群多样性，有效地抑制了早熟收敛；随后进行的全局大变异保证整个过程全局收敛。仿真实验用典型的测试函数验证了此算法能显著提高解的质量和收敛速度。

Study of improved genetic algorithm based on dual mutation and its simulation

An improved genetic algorithm based on dual mutation is proposed to overcome slow convergent speed,poor seeking optimization capabilities and easy to fall into a local optimum of the standard genetic algorithm.In the evolution process,a concept of generalized hamming distance is introduced.When the crossover operator which is controlled by the generalized hamming distance have individuals less than the population size,a small local mutation carries on original population,which can avoid inbreeding,expand the search space,increase the population diversity and effectively curb the premature convergence.Following,the overall big mutation ensures global convergence of the whole process.The simulation with the typical test functions indicates that this new genetic algorithm can significantly improve the quality of solutions and convergence speed.