摘要
本文分析了基本遗传算法全局搜索效率不高的内在原因 ,提出了基于相似个体交叉和 (μ+λ)选择机制的小生境并行进化技术 ,从理论上论证了该技术不但能提供最强的选择压 ,而且能维持足够的种群多样性 .对高维不连续函数和高维多峰函数优化的仿真实验结果表明 ,应用该技术能显著地改善遗传算法的全局收敛可靠性和收敛速度 。
This paper analyses the intrinsic causes of low efficiency searching the global optimum by genetic algorithms. A class of parallelism evolution technique for niches implemented by crossover of similar individuals and (μ+λ) selected mechanism are proposed. It was proved theoretically and analytically that this kind of niche technique can provide strong selected pressure and also maintain the diversity of individuals in populations. The results of simulation experiments of minimizing discontinuous and multimodal functions with higher dimensions by using genetic algorithms introducing the niche mechanism show that it can remarkably improve the reliability of global convergence and converging velocity, and offer practical facts to justify the design and application of this niche mechanism.
出处
《信息与控制》
CSCD
北大核心
2001年第6期526-530,542,共6页
Information and Control
基金
国家自然科学基金资助项目 项目号 :5 983 5 170
