基于熵的双群体遗传算法研究

Entropy Based Genetic Algorithm with Dual Subpopulations

提出了一种基于熵的双群体遗传算法,首先采用熵最大的方法产生两个初始化群体,使得初始化的个体尽可能均匀分布在遗传搜索空间。在一个群体中设计了基于熵最大的直接替代选择运算并采用高变异率提高遗传算法的全局探索能力。在另一个群体中采用逐渐减小的动态变异率提高遗传算法的快速局部搜索能力。两个群体之间的相互移民策略均衡了遗传算法的全局探索能力和快速局部搜索能力。实验显示,基于熵的双群体遗传算法对复杂多模函数寻优在全局收效率和收敛速度上都具有一定的优势。

An entropy based genetic algorithm with dual subpopulations is proposed in this paper. Two separated subpopulations are generated by using the information entropy theory, and the maximum entropy of genetic population helps to obtain the diversified individuals. In one population, high mutation rate and entropy based selection enhance the exploration ability. In the other population, dynamic decreased mutation rate is adopted in order to obtain good exploitation ability. Also the immigration between the two populations balances the exploration and exploitation ability. The experimental results show that the proposed method can gain higher global convergence rate and higher speed.