A Genetic Relation Algorithm and Its Application to the Portfolio Optimization
The survey of the relevant literature showed that there have been many studies for portfolio optimization problem and that the number of studies which have investigated the optimum portfolio using evolutionary computation is quite high.But almost none of these studies deals with genetic relation algorithm (GRA). This study presents an approach to large-scale portfolio optimization problem using GRA with a new operator, called guided mutation. In order to pick up the most efficient portfolio, GRA considers the correlation coefficient between stock brands as strength, which indicates the relation between nodes in each individual of GRA. Guided mutation generates offspring according to the average value of correlation coefficients in each individual. A genetic relation algorithm with guided mutation (GRA/G) for the portfolio optimization is proposed in this paper. Genetic network programming (GNP), which was proposed in our previous research, is used to validate the performance of the portfolio generated with GRA/G. The results show that GRA/G approach is successful in portfolio optimization.
Yan Chen Kotaro Hirasawa
Graduate school of Information, Production, and Systems Waseda University, 2-7, Hibikino Wakamatsu-ku, Kitakyushu, Fukuoka
国际会议
The 10th International Conference on Intelligent Technologies(第十届智慧科技国际会议 InTech09)
桂林
英文
8-13
2009-12-12（万方平台首次上网日期，不代表论文的发表时间）