| A Multi-Objective Genetic Algorithm for Optimal Portfolio Problems |
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| 作者姓名： | 林丹 赵瑞 |
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| 作者单位： | SchoolofSciences,TianjinUniversity,Tianjin300072,China |
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| 基金项目： | SupportedbyNationalNaturalScienceFoundationofChina(No .70 3 0 10 0 5)andLiuHuiCenterofAppliedMathematicsofNankaiUniversityandTianjinUniversity |
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| 摘 要： | This paper concerns with modeling and design of an algorithm for the portfolio selection problems with fixed transaction costs and minimum transaction lots. A mean-variance model for the portfolio selection problem is proposed, and the model is formulated as a non-smooth and nonlinear integer programming problem with multiple objective functions. As it has been proven that finding a feasible solution to the problem only is already NP-hard, based on NSGA-II and genetic algorithm for numerical optimization of constrained problems (Genocop), a multi-objective genetic algorithm (MOGA) is designed to solve the model. Its features comprise integer encoding and corresponding operators, and special treatment of constraints conditions. It is illustrated via a numerical example that the genetic algorithm can efficiently solve portfolio selection models proposed in this paper.This approach offers promise for the portfolio problems in practice.
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| 关 键 词： | 多目标遗传算法 最优证券问题 非线性整数规划 有价证卷选择 交易费用 |
A Multi-Objective Genetic Algorithm for Optimal Portfolio Problems |
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| LIN Dan,ZHAO Rui.A Multi-Objective Genetic Algorithm for Optimal Portfolio Problems[J].Transactions of Tianjin University,2004,10(4):310-314. |
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| Authors: | LIN Dan ZHAO Rui |
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| Abstract: | This paper concerns with modeling and design of an algorithm for the portfolio selection problems with fixed transaction costs and minimum transaction lots. A mean-variance model for the portfolio selection problem is proposed, and the model is formulated as a non-smooth and nonlinear integer programming problem with multiple objective functions. As it has been proven that finding a feasible solution to the problem only is already NP-hard, based on NSGA-II and genetic algorithm for numerical optimization of constrained problems (Genocop), a multi-objective genetic algorithm (MOGA) is designed to solve the model. Its features comprise integer encoding and corresponding operators, and special treatment of constraints conditions. It is illustrated via a numerical example that the genetic algorithm can efficiently solve portfolio selection models proposed in this paper. This approach offers promise for the portfolio problems in practice. |
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| Keywords: | portfolio selection transaction costs minimum transaction lots genetic algorithm |
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