摘要
遗传算法(GA)作为一种新型的智能优化方法,以其结构简单、适应性强等特点在众多实际领域取得了成功的应用,但存在计算复杂度大、易于局部收敛等方面的不足。本文在分析现有遗传操作的不足和生物进化的基本特征基础上,从提高进化效率的角度出发,提出基于多保留策略的复合型遗传算法(简称MRS-CGA);进而利用Markov链理论和仿真技术,从不同的层面分析了算法的性能。讨论结果表明,算法从本质上推广了常规的GA,在计算效率和收敛性能上均明显地优于常规的GA。
As a new kind of intelligence optimization method, genetic algorithm, with the features of simple structure and strong adaptability, achieves great success in many real fields, but still there are some shortcomings such as greater computation complexity and more chance of being trapped into local states. This paper analyzes the deficiency of the existing genetic operation and the essential characteristics of creature evolution to improve evolution efficiency, and proposes a composite genetic algorithm based on multi- reserving strategy （MRS - CGA for short）. Moreover, it analyzes the performances of MRS - CGA by the theory of Markov chains and simulation technology. All the results indicate that, MRS- CGA is essentially the extension of ordinary GA, and obviously better than ordinary GA in computation efficiency and convergence performance.
出处
《河北工程大学学报（自然科学版）》
CAS
2010年第1期103-108,共6页
Journal of Hebei University of Engineering:Natural Science Edition
基金
河北省自然科学基金项目(F2009000857)
关键词
遗传算法
复合型遗传算法
多保留策略
收敛性
MARKOV链
genetic algorithm
composite genetic algorithm
multi- reserved strategy
convergence
Markov chain
