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A New Multi-Objective Genetic Algorithm for Use in Investment Management

by Simona Dinu, Gabriela andrei
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 58 - Number 13
Year of Publication: 2012
Authors: Simona Dinu, Gabriela andrei
10.5120/9339-3656

Simona Dinu, Gabriela andrei . A New Multi-Objective Genetic Algorithm for Use in Investment Management. International Journal of Computer Applications. 58, 13 ( November 2012), 1-8. DOI=10.5120/9339-3656

@article{ 10.5120/9339-3656,
author = { Simona Dinu, Gabriela andrei },
title = { A New Multi-Objective Genetic Algorithm for Use in Investment Management },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 58 },
number = { 13 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume58/number13/9339-3656/ },
doi = { 10.5120/9339-3656 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:02:22.696783+05:30
%A Simona Dinu
%A Gabriela andrei
%T A New Multi-Objective Genetic Algorithm for Use in Investment Management
%J International Journal of Computer Applications
%@ 0975-8887
%V 58
%N 13
%P 1-8
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The portfolio optimization problem is an important management issue in financial economics. Its aim is to calculate an optimal asset allocation that satisfy specific investment goals, out of a given investment plan. In the past few years, more and more attention is given in applying Evolutionary Computation in solving complex optimization problems. The use of Multi-Objective Evolutionary Algorithms - MOEA in practical problems involving multi-objective optimizations is not restricted to a strict application of an existing algorithm described in literature. Oftenly, for a certain problem, one preferres an algorithm' design that includes strategies characterizing different important algorithms used in the MOEA field. The main objective of this study was to develop an efficient and effective portfolio selection Multi-Objective Genetic Algorithm. Experimental tests presented for five benchmark data sets are given to demonstrate significant advantages regarding the solution quality and the speed of the algorithm.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithms Portfolio optimization Efficient frontier Mean-Variance

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