An evolutionary multi-objective optimization algorithm for portfolio selection problem

dc.contributor.author	Guillermo Cabrera G.
dc.contributor.author	Vasconcellos C.
dc.contributor.author	Soto R.
dc.contributor.author	Rubio J.M.
dc.contributor.author	Paredes F.
dc.contributor.author	Crawford B.
dc.date.accessioned	2020-09-02T22:19:31Z
dc.date.available	2020-09-02T22:19:31Z
dc.date.issued	2011
dc.identifier.citation	6, 22, 5317-5328
dc.identifier.issn	19921950
dc.identifier.uri	https://hdl.handle.net/20.500.12728/4766
dc.description	Cultural algorithms (CAs) are one of the metaheuristics which can be adapted in order to work in multiobjective optimization environments. On the other hand, portfolio selection problem (PSP) is a wellknow problem in literature. However, only a few articles have applied evolutionary multi-objective (EMO) algorithms to these problems and articles presenting CAs applied to the PSP have not been found. In this article, we present a bi-objective cultural algorithm (BOCA) which has been applied to the PSP, and obtaining acceptable results in comparison with other well-known EMO algorithms from the literature. The considered criteria of the problem are risk minimization and profit maximization. The different solutions obtained with the BOCA have been compared using max-delta-area metric. © 2011 Academic Journals.
dc.language.iso	en
dc.publisher	Academic Journals
dc.subject	Autonomous search
dc.subject	Constraint programming
dc.subject	Heuristic search
dc.title	An evolutionary multi-objective optimization algorithm for portfolio selection problem
dc.type	Article

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