A Knowledge-based Genetic Algorithm to the Global Numerical Optimization

摘要：

Global optimization algorithms have received much attention recently. This paper presented a Knowledge-based Genetic Algorithm (KGA) for the global numerical optimization. In KGA, some innovative operators was proposed by integrating the empirical knowledge with the existing operation. In particular, we proposed two novel operators: knowledge-based mutation operator based on round or immunity operation, and knowledge-based local search operator based on sensitivity analysis and steepest descent method. The experimental results suggest that KGA outperforms to some published algorithms.

作者: Tie-Jun Zhou Li-Ning Xing

作者单位: School of computer and communication Hunan University Changsha 410082, P.R.China College of Information System and Management National University of Defense Technology Changsha 4100

会议类型: 国际会议

会议名称: The Second International Joint Conference on Computational Science and Optimization(CSO 2009)(2009 国际计算科学与优化会议)

会议地点: 三亚

会议语种:英文

页码: 513-516

在线出版日期: 2009-04-24（万方平台首次上网日期，不代表论文的发表时间）