Abstract
Hybrid intelligent algorithms, especially those who combine nature-inspired techniques, are well known for their searching abilities in complex problem domains and their performance. One of their main characteristic is that they manage to escape getting trapped in local optima. In this study, two hybrid intelligent schemes are compared both in terms of performance and convergence ability in a complex financial problem. Particularly, both algorithms use a type of genetic algorithm for asset selection and they differ on the technique applied for weight optimization: the first hybrid uses a numerical function optimization method, while the second one uses a continuous ant colony optimization algorithm. Results indicate that there is great potential in combining characteristics of nature-inspired algorithms in order to solve NP-hard optimization problems.
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
Byrd, R.H., Gilbert, J.C., Nocedal, J.: A trust region method based on interior point techniques for nonlinear programming. Mathematical Programming 89(1), 149–185 (2000)
Giannakouris, G., Vassiliadis, V., Dounias, G.: Experimental Study on a Hybrid Nature-Inspired Algorithm for Financial Portfolio Optimization. In: Konstantopoulos, S., Perantonis, S., Karkaletsis, V., Spyropoulos, C.D., Vouros, G. (eds.) SETN 2010. LNCS (LNAI), vol. 6040, pp. 101–111. Springer, Heidelberg (2010)
Holland, J.H.: Genetic Algorithms. Scientific American, 66–72 (1992)
Jeurissen, R., Berg, J.: Optimized index tracking using a hybrid genetic algorithm. In: IEEE Congress on Evolutionary Computation, pp. 2327–2334 (2008)
Kuhn, J.: Optimal risk-return tradeoffs of commercial banks and the suitability of profitability measures for loan portfolios. Springer, Berlin (2006)
Maringer, D., Kelleler, H.: Optimization of Cardinality Constrained Portfolios with a Hybrid Local Search Algorithm. OR Spectrum 25, 481–495 (2003)
Sharpe, W.F.: The Sharpe ratio. Journal of Portfolio Management, 49–58 (1994)
Socha, K., Dorigo, M.: Ant colony optimization for continuous domains. European Journal of Operational Research 185, 1155–1173 (2008)
Thomaidis, N.S., Angelidis, T., Vassiliadis, V., Dounias, G.: Active Portfolio Management with Cardinality Constraints: An Application of Particle Swarm Optimization. New Mathematics and Natural Computation, Working Paper (2008)
Vassiliadis, V., Thomaidis, N., Dounias, G.: Active Portfolio Management under a Downside Risk Framework: Comparison of a Hybrid Nature – Inspired Scheme. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds.) HAIS 2009. LNCS, vol. 5572, pp. 702–712. Springer, Heidelberg (2009)
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vassiliadis, V., Thomaidis, N., Dounias, G. (2011). On the Performance and Convergence Properties of Hybrid Intelligent Schemes: Application on Portfolio Optimization Domain. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2011. Lecture Notes in Computer Science, vol 6625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20520-0_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-20520-0_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20519-4
Online ISBN: 978-3-642-20520-0
eBook Packages: Computer ScienceComputer Science (R0)