摘要
通过分析交叉点规模对交叉算子空间搜索性能的影响,可以发现在遗传算法的搜索过程中,其对交叉点规模的需求是随群体状态的演变而动态变化的.为实现对交叉点规模的优化,提出使用分阶段调整策略、随机分配策略以及自适应进化策略3种方法来完成对交叉点规模的动态调控.对典型高维函数的优化实验表明,上述方法可以显著提高交叉操作的搜索效率,其中,自适应进化策略利用搜索机制可以发现一类高维函数交叉点规模的控制知识,实验结果证实了此类知识的有效性.此外,该研究也为对进化算法中算子和参数的优化提供了新思路.
Based on the analysis of relationship between the crossover scale and reachable subspace of crossover operator, it can be found that the crossover scale is dynamically adjusted to the population structure. In this paper, three control mechanisms-the well-phased control strategy, the random distribution strategy and the adaptation evolution strategy are built up to adjust the crossover scale. The simulation tests of the classical function show these optimization mechanisms are available and valuable control knowledge of crossover scale for multi-dimension functions have been generated by the adaptation evolution strategy. Furthermore, this research suggests a new method for the operator and parameter optimization of evolution algorithm.
出处
《软件学报》
EI
CSCD
北大核心
2009年第4期890-901,共12页
Journal of Software
基金
国家自然科学基金Nos.60743009,60501006
陕西省自然科学基金No.2006F-43~~
关键词
遗传算法
交叉算子
优化交叉点规模
genetic algorithm
crossover operator
optimization crossover scale
