Knowledge-Based Genetic Algorithms

摘要：

In this paper,Rough Set Theory (RST) was introduced to discover knowledge hidden in the evolution process of Genetic Algorithm.Firstly it was used to analyze correlation between individual variables and their fitness function.Secondly,eigenvector was defined to judge the characteristic of the problem.And then the knowledge discovered was used to select evolution subspace and to realize knowledge-based evolution.Experiment results have shown that the proposed method has higher searching efficiency,faster convergent speed,and good performance for deceptive problem and multi-modal problems.

关键词： Rough set theory (RST) Genetic Algorithms (GAs) Knowl-edge discovery Knowledge evolution Eigenvector

作者: Gaowei Yan Gang Xie Zehua Chen Keming Xie

作者单位: College of Information Engineering,Taiyuan University of Technology Taiyuan,Shanxi,P.R.China,030024

会议类型: 国际会议

会议名称: The Third International Conference on Rough Sets and Knowledge Tevhnology(RSKT 2008)(第三届粗糙集与知识技术国际会议)

会议地点: 成都

会议语种:英文

页码: 148-155

在线出版日期: 2008-05-17（万方平台首次上网日期，不代表论文的发表时间）