探索曲面结构的小生境遗传算法

Niching genetic algorithms exploring structure of landscape

提出了具有适应值曲面结构自学习能力的多区域并行局部搜索算子PLS和约束交叉算子GC,定性地分析了它们的作用机制,给出了基于仿真实验数据的遗传算法收敛速度和全局收敛可靠性的定量计算方法.仿真实验数据表明,PLS能有效地提高搜索速度并维持足够的种群基因多样度,GC可有效地微调解的质量,使改进的遗传算法的全局收敛速度和收敛可靠性均显著地优于标准遗传算法,并具有良好的鲁棒性和稳定性.

This paper proposes a kind of parallel local search operator PLS and a restricted crossover operator GC having self_learning ability of the structure of fitness landscape, and qualitatively analyzes the operation mechanisms. Several formulas based on empirical data for calculating convergence velocity and global convergence reliability are firstly presented. A large number of experiments on several typical functions show that PLS is not only able to sharply speed up searching but also sufficiently maintain genotypic diversity in populations, and GC can precisely tune solutions in the late stage of searching. Both the convergence velocity and the global convergence reliability of the improved GAs introducing PLS and GC excel greatly that of standard ones, and have good robustness and stability.