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A hybrid algorithm for constrained portfolio selection problems
journal contribution
posted on 2023-07-26, 13:58 authored by Khin LwinSince Markowitz’s seminal work on the mean-variance model in modern portfolio theory, many studies have been conducted on computational techniques and recently meta-heuristics for portfolio selection problems. In this work, we propose and investigate a new hybrid algorithm integrating the population based incremental learning and differential evolution algorithms for the portfolio selection problem. We consider the extended mean-variance model with practical trading constraints including the cardinality, floor and ceiling constraints. The proposed hybrid algorithm adopts a partially guided mutation and an elitist strategy to promote the quality of solution. The performance of the proposed hybrid algorithm has been evaluated on the extended benchmark datasets in the OR Library. The computational results demonstrate that the proposed hybrid algorithm is not only effective but also efficient in solving the mean-variance model with real world constraints.
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Refereed
- Yes
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39Issue number
2Page range
251-266Publication title
Applied IntelligenceISSN
1573-7497External DOI
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SpringerLanguage
- other
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2016-11-09Legacy creation date
2016-11-07Legacy Faculty/School/Department
ARCHIVED Faculty of Science & Technology (until September 2018)Usage metrics
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