基于基因分析调控思想的遗传算法

在微海明距离、爬山法和进化规划的思想基础上,提出了通过分析每一代的染色体的基因和基因块信息,采用综合调控策略和隐形的处理好的基因和基因块,使得快速性和多样性指标都得到满足;在保持快速收敛于最优的同时,又不失种群的多样性,使得整个进化一直处于调控机制下,提高了算法的寻优效率。使用Matlab通过对4个测试函数的仿真验证,这种新型的遗传算法的寻优性能较之SGA(基本遗传算法)有了很大的提高。

Improved GA Based on Schemata Analysis and Control

On the basis of ideas such as Hamming Distance, Hill Climbing and Evolutionary Strategy, a method is presented to analyze genes and schemata in every generation so that the requirements of speed and diversity can be met. So the whole evolution is always under this method control,and the searching rate in oplimization of the algorithm is increased. After authentication of four test functions by using Matlab, this new GA is better than SGA.