Abstract
We use local search to improve the performance of Genetic Algorithms applied the problem of Financial Portfolio Selection and Optimization. Our work describes the Tree based Genetic Algorithm for Portfolio Optimization. To improve this evolutionary system, we introduce a new guided crossover operator, which we call the BWS, and add a local optimization step. The performance of the system increases noticeably on simulated experiments with historical data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aranha, C., Iba, H.: A tree-based ga representation for the portfolio optimization problem. In: GECCO - Genetic and Evolutionary Computation Conference. ACM Press, New York (July 2008)
Ullah, B., Sarker, R., Cornforth, D., Lokan, C.: An agent-based memetic algorithm (ama) for solving constrained optimization problems. In: IEEE Congress on Evolutionary Computation (CEC), Singapore, September 2007, pp. 999–1006 (2007)
Markowitz, H.: Mean-Variance analysis in Portfolio Choice and Capital Market. Basil Blackwell, New York (1987)
Lyu, Y.-D.: Financial Engineering and Computation. Cambridge Press, Cambridge (2002)
Hochreiter, R.: An evolutionary computation approach to scenario-based risk-return portfolio optimization for general risk measures. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 199–207. Springer, Heidelberg (2007)
Lipinski, P., Winczura, K., Wojcik, J.: Building risk-optimal portfolio using evolutionary strategies. In: Giacobini, M., et al. (eds.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 208–217. Springer, Heidelberg (2007)
Aranha, C., Iba, H.: Modelling cost into a genetic algorithm-based portfolio optimization system by seeding and objective sharing. In: Proc. of the Conference on Evolutionary Computation, pp. 196–203 (2007)
Streichert, F., Ulmer, H., Zell, A.: Evolutionary algorithms and the cardinality constrained portfolio optimization problem. In: Ahr, D., Fahrion, R., Oswald, M., Reinelt, G. (eds.) Operations Research Proceedings. Springer, Heidelberg (September 2003)
Werner, J.C., Fogarti, T.C.: Genetic control applied to asset managements. In: Foster, J., et al. (eds.) EuroGP 2002. LNCS, vol. 2278, pp. 192–201. Springer, Heidelberg (2002)
Yan, W., Clack, C.D.: Evolving robust gp solutions for hedge fund stock selection in emerging markets. In: GECCO 2007 - Genetic and Evolutionary Computation Conference, London, England. ACM Press, New York (July 2007)
Nikolaev, N.Y., Iba, H.: Regularization approach to inductive genetic programming. IEEE Transactions on evolutionary computation 5(4), 359–375 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aranha, C., Iba, H. (2008). Application of a Memetic Algorithm to the Portfolio Optimization Problem. In: Wobcke, W., Zhang, M. (eds) AI 2008: Advances in Artificial Intelligence. AI 2008. Lecture Notes in Computer Science(), vol 5360. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89378-3_52
Download citation
DOI: https://doi.org/10.1007/978-3-540-89378-3_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89377-6
Online ISBN: 978-3-540-89378-3
eBook Packages: Computer ScienceComputer Science (R0)