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Genetic algorithm designed for solving portfolio optimization problems subjected to cardinality constraint


  • Hemant Jalota

    () (Indian Institute of Technology Mandi)

  • Manoj Thakur

    () (Indian Institute of Technology Mandi)


Abstract In the present study, a new algorithm named BEXPM-RM is proposed which require no constraint handling techniques to solve portfolio optimization problems subjected to budget, cardinality, and lower/upper bound constraints. The algorithm presented combines the BEX-PM (Thakur et al. in Appl Math Comput 235:292–317, 2014) genetic algorithm (GA) together with repair mechanism (RM) proposed by Chang et al. (Comput Oper Res 27(13):1271–1302, 2000). BEXPM GA tries to efficiently explore the search space whereas repair method suggested by Chang et al. (2000) ensures that a solution string is always feasible subject to the budget, cardinality, and lower/upper bound constraints. To analyze the performance of BEXPM-RM, six portfolio optimization problems are considered from the literature (Chang et al. 2000; Barak et al. in Eur J Oper Res 228(1):141–147, 2013). Among these one problem uses fuzzy set theory and others used probability theory to quantify attributes of a portfolio. In addition to these problems, a new portfolio model is formulated in fuzzy environment to analyze the effect of providing different sets of lower or/and upper bound to an asset.

Suggested Citation

  • Hemant Jalota & Manoj Thakur, 2018. "Genetic algorithm designed for solving portfolio optimization problems subjected to cardinality constraint," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(1), pages 294-305, February.
  • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:1:d:10.1007_s13198-017-0574-z
    DOI: 10.1007/s13198-017-0574-z

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    References listed on IDEAS

    1. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    2. Barak, Sasan & Abessi, Masoud & Modarres, Mohammad, 2013. "Fuzzy turnover rate chance constraints portfolio model," European Journal of Operational Research, Elsevier, vol. 228(1), pages 141-147.
    3. Markowitz, Harry, 2014. "Mean–variance approximations to expected utility," European Journal of Operational Research, Elsevier, vol. 234(2), pages 346-355.
    4. Leon, T. & Liern, V. & Vercher, E., 2002. "Viability of infeasible portfolio selection problems: A fuzzy approach," European Journal of Operational Research, Elsevier, vol. 139(1), pages 178-189, May.
    5. N. J. Jobst & M. D. Horniman & C. A. Lucas & G. Mitra, 2001. "Computational aspects of alternative portfolio selection models in the presence of discrete asset choice constraints," Quantitative Finance, Taylor & Francis Journals, vol. 1(5), pages 489-501.
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