CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

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

by Simona Dinu, Gabriela andrei
journal cover thumbnail

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
  1. Busetti, F. R. 2000. Metaheuristic approaches to realistic portfolio optimisation. Master thesis. University of South Africa.
  2. Coello, C. A. , Lamont, G. B. and Van Veldhuizen, D. A. 2007. Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, New York.
  3. Portfolio selection benchmark data set at http://people. brunel. ac. uk/~mastjjb/jeb/orlib/files.
  4. Sefiane, S. and Benbouziane, M. 2012. Portfolio Selection Using Genetic Algorithm, Journal of Applied Finance & Banking, Vol. 2(4), 143-154.
  5. Shoaf, J. and Foster, J. A. 1998. The efficient set GA for stock portfolios. In Proceedings of the 1998 IEEE International Conference on Evolutionary Computation (CEC'98). Anchorage, Alaska: IEEE Press, 354–359.
  6. Vedarajan, G. , Chan, L. C. and Goldberg, D. E. 1997. Investment Portfolio Optimization using Genetic Algorithms. In Proceedings of the Genetic Programming 1997 Conference, J. R. Koza, Ed. Stanford University, California: Stanford Bookstore, 255–263.
  7. Chiranjeevi, C. and Sastry, V. N. 2007. Multi-objective portfolio optimization models and its solution using genetic algorithms. In Proceedings of the Computational Intelligence and Multimedia Applications, 453–457.
  8. Branke J. and Deb, K. 2005. Integrating User Preferences into Evolutionary Multi-Objective Optimization. In Yaochu Jin (editor), Knowledge Incorporation in Evolutionary Computation, Springer, 461-477.
  9. Kukkonen, S. and Deb, K. 2007. An empirical scalability study of a non-dominated solutions pruning algorithm. In Late Breaking Papers Proceedings of the Fourth International Conference on Evolutionary Multi-Criterion Optimization EMO 2007, Matsushima, Japan.
  10. Schaffer, J. D. 1985. Multiple objective optimization with vector evaluated genetic algorithms. In Proc. 1st ICGA, 1985, 93–100.
  11. Back, T. and Schutz, M. 1996. Intelligent mutation rate control in canonical genetic algorithms, Proc. of the Ninth International Symposium on Methodologies for Intelligent Systems, 158-167.
  12. Van Veldhuizen, D. A. and Lamont, G. B. 2000. On measuring multi-objective evolutionary algorithm performance. In Proceedings of the 2000 Congress on Evolutionary Computation, 204–211.
  13. Schott, J. R. 1995. Fault Tolerant Design Using Single and Multi-criteria Genetic Algorithm Optimization, M. S. Thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA.
  14. Corne, D. W. , Knowles, J. D. and Oates, M. J. 2000. The pareto envelope-based selection algorithm for multi-objective optimization. In M. S. et al. (Ed. ), Parallel Problem Solving from Nature – PPSN VI, Berlin, Springer, 839–848.
  15. Mishra, S. K. , Panda, G. , Majhi, B. and Majhi, R. 2012. Improved Portfolio Optimization Combining Multi-objective Evolutionary Computing Algorithm and Prediction Strategy. Proceedings of the World Congress on Engineering, July 4 - 6, 2012, London, U. K. , Vol. I, 470-474.
Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithms Portfolio optimization Efficient frontier Mean-Variance

Powered by PhDFocusTM