A New Population Initialization Approach Based on Bordered Hessian for Portfolio Optimization Problems
A New Population Initialization Approach Based on Bordered Hessian for Portfolio Optimization Problems
复制标题
解决投资组合优化问题的基于有界Hessian的新种群初始化方法
DOI：
10.1109/smc.2013.232
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Hisashi Yamamoto
中科院分区：
文献类型：
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作者：
Yukiko Orito;Yoshiko Hanada;Shunsuke Shibata;Hisashi Yamamoto
在投资组合优化问题中，投资组合中的比例加权组合被表示为一个介于0和1之间的实数组。然而，当应用任何进化算法时，该算法几乎不取给定的实值的末尾。这意味着进化算法存在一个问题，即它们不能给出权重表示为0的未选择的资产。为了避免这一问题，我们提出了一种新的利用边界黑森极点的种群初始化方法，并将该方法应用于求解投资组合优化问题的遗传算法的初始种群。在数值实验中，我们的方法使用了种群初始化方法和遗传算法，在投资组合由大量资产组成的情况下，我们的方法对投资组合的优化非常有效。
In the portfolio optimization problems, the proportion-weighted combination in a portfolio is represented as a real-valued array between 0 and 1. While applying any evolutionary algorithm, however, the algorithm hardly takes the ends of a given real value. It means that the evolutionary algorithms have a problem that they cannot give the not-selected asset whose weight is represented as 0. In order to avoid this problem, we propose a new population initialization approach using the extreme point of the bordered Hessian and then apply our approach to the initial population of GA for the portfolio optimization problems in this paper. In the numerical experiments, we show that our method employing the population initialization approach and GA works very well for the portfolio optimizations even if the portfolio consists of the large number of assets.