一种基于种群熵估计的自适应遗传算法

Adaptive genetic algorithm based on population entropy estimating

为获得运行过程中对搜索空间勘探和开采的平衡 ,该文提出了一种基于种群熵估计的参数自适应遗传算法。该算法每一进化代的新种群由保留、繁殖和随机 3部分子种群组成 ,其数量则由相应的参数进行控制。通过引入种群熵的概念对种群内个体的多样性进行度量并使用一种简单的方法对其进行估计以确定各控制参数 ,该算法实现了参数的自适应调节。试验结果表明该算法能够有效协调勘探和开采 。

This paper presents a parameter adaptive genetic algorithm based on the entropy estimating to balance exploration and exploitation in the problem's solution space while doing optimization. In the algorithm, the new population in each generation consists of three sub populations: a preserved part, a reproduced part and a randomized part with corresponding parameters introduced to control the size of each part. The parameters can be adjusted adaptively by incorporating population entropy into the algorithm to provide a quantitative measure of the diversity of individuals in the population and by adopting a simple yet practical method to estimate the entropy of a given population. Experimental data show that the algorithm can effectively balance the exploration and exploitation and provides excellent performance with complex problems.