Multi-Objective Indicator Based Evolutionary Algorithm for Portfolio optimization

被引：0

作者：

Bhagavatula, Sowmya Sree ^{[1
]}

Sanjeevi, Sriram G. ^{[1
]}

Kumar, Divya ^{[1
]}

Yadav, Chitranjan Kumar ^{[1
]}

机构：

[1] NIT Warangal, Dept Comp Sci & Engn, Warangal, Andhra Pradesh, India

来源：

SOUVENIR OF THE 2014 IEEE INTERNATIONAL ADVANCE COMPUTING CONFERENCE (IACC) | 2014年

关键词：

multiobjective; portfolio optimization; hypervolume indicator; evolutionary algorithm; mating; survivor selection; crowded comparison; SELECTION;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Portfolio optimization is a standard problem in the financial world for making investment decisions which involve investing into a variety of assets with the aim of maximizing yield and minimizing risk. Modern portfolio theory is a mathematical approach to the problem that endeavors to accomplish a plausive portfolio by giving best weighting of the assets. In this study, an indicator based evolutionary algorithm (IBEA) has been compared with two well known evolutionary algorithms-Non-dominated Sorting Genetic Algorithm II(NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA-II). The results reveal that IBEA outperforms the other two algorithms in terms of its closeness to the true pareto front. Also, a diversity enhanced version of IBEA (IBEA-D) is proposed, which is found to be providing more diverse solutions than IBEA.

引用

页码：1206 / 1210

页数：5

共 15 条

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