自适应引导进化遗传算法

该文提出一种自适应引导进化遗传算法。算法中采用佳点集方法产生初始种群，结合保留精英个体策略，对种群进行分割，各子种群并行交叉变异，且其中一个子种群为随机产生的。为提高算法收敛速度，分别对各子种群中较优个体进行优秀基因位统计，据此对其它个体采取一种自适应引导变异操作。通过将算法运行过程建模为有限齐次马氏链，证明了算法的全局收敛性和收敛快速性。实验结果表明，自适应引导进化遗传算法较其它的遗传算法在收敛速度和准确度上都有较大提高。

Guided Self-adaptive Evolutionary Genetic Algorithm

A Guided Self-adaptive Evolutionary Genetic Algorithm (GSEGA) is proposed. The principle of good point set is used to generate the initial population. Based on the elitist preserved method, a way of parallel crossing and mutation with population-segmentation is offered, in which a son population among the segmented population is randomly generated. In addition, a guided self-adaptive mutation strategy based on the statistics of the more excellent individualities is adopted on the other part of the son population to speed up the evolution. Through the use of the homogeneous finite Markov chain model, the global convergence and high searching speed of the GSEGA is proved. The experimental results show that the GSEGA presents a higher speed and precision in comparison with the other Genetic Algorithms (GAs).