STUDY ON CONVERGENCE OF SELF-ADAPTIVE AND MULTI-POPULATION COMPOSITE GENETIC ALGORITHM

摘要：

In view of the slowness and the locality of convergence for Basic Genetic Algorithm (BCA for short) in solving complex optimization problems, we proposed an improved genetic algorithm named self-adpative and multi-population composite genetic algorithm (SM-CGA for short), and gave the structure and implementation steps of the algorithm; then we consider its global convergence under the elitist preserving strategy using Markov chain theory, and analyze its performance through three examples from different aspects. All of the results indicate that the new algorithm possess interesting advantages such as better convergence, less chance trapping into premature states, so it can be widely used in many large-scale and high-accuracy optimization problems.

关键词： Basic genetic algorithm Self-adaptive operator Milti-population Composite genetic algorithm Convergence Markov chain

作者: LI-MIN LIU NIAN-PENG WANG FA-CHAO LI

作者单位: School of Science, Hebei University of Engineering, Handan 056038, China School of Economics and Management, Hebei University of Science and Technology, Shijiazhuang 050018,

会议类型: 国际会议

会议名称: 2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

会议地点: 保定

会议语种:英文

页码: 2680-2685

在线出版日期: 2009-07-12（万方平台首次上网日期，不代表论文的发表时间）