Abstract
Portfolio Selection (PS) is to allocate a given amount of investment fund across a set of assets in such a way that the return is maximized and the risk is minimized. PS is a challenging financial engineering problem and optimization problem. GA is well known for its effectiveness in solving optimization problems. However it may experience slow convergence especially when dealing with constrained optimization problems. To address this issue, we propose a variation of genetic algorithm (GA), which utilizes dual populations to solve PS problems. The first population is responsible for exploration in the search space, whilst the second one is for exploitation to speed up the convergence process. These two populations share individuals periodically. The proposed algorithm has been tested on the standard PS benchmark instances. The results reveal that our method can obtain very good results compared to the state of the art methods. More importantly, this dual population method is much faster than other methods.
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Sabar, N.R., Song, A. (2014). Dual Population Genetic Algorithm for the Cardinality Constrained Portfolio Selection Problem. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_59
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DOI: https://doi.org/10.1007/978-3-319-13563-2_59
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13562-5
Online ISBN: 978-3-319-13563-2
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