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. © 2008 Springer Berlin Heidelberg.
Cite
CITATION STYLE
Aranha, C., & Iba, H. (2008). Application of a memetic algorithm to the portfolio optimization problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5360 LNAI, pp. 512–521). https://doi.org/10.1007/978-3-540-89378-3_52
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