基于搜索空间分割的协同进化遗传算法  

Co-evolutionary Genetic Algorithm Based on Search Space Partition

作　　者：郭方炜 许峰 

机构地区：[1]安徽理工大学数学与大数据学院,安徽淮南232001

出　　处：《软件导刊》2018年第1期92-94,98,共4页Software Guide

基　　金：安徽省教育厅自然科学基金项目(2014KB236)

摘　　要：针对经典协同进化遗传算法在优化大决策空间问题时计算复杂度较高的问题,提出了一种基于搜索空间分割的协同进化遗传算法,其基本思想是:将种群分割为不同规模的子种群,在进化过程中应用ε自适应方法调整子种群规模。复杂度分析和数值实验表明,改进后的算法可降低算法计算量,提高算法的优化效率。In order to solve the problem of high computational complexity when the classical co evolutionary genetic algorithm is used to optimize the large decision space problem, a cooperative evolutionary genetic algorithm based on the search space segmentation is proposed. The basic idea is that the population is divided into sub populations with different scales, and the e adaptive method is used to adjust the size of the sub population in the evolutionary process. Complexity analysis and numerical experiments show that the improved algorithm can reduce the computational complexity and optimize the efficiency of the algorithm.

关 键 词：遗传算法 协同进化 空间分割 ε自适应调整 算法效率 

分 类 号：TP312[自动化与计算机技术—计算机软件与理论]