A Comparative Study of Multi-objective Evolutionary Algorithms to Optimize the Selection of Investment Portfolios with Cardinality Constraints

  • Feijoo E. Colomine Duran
  • Carlos Cotta
  • Antonio J. Fernández-Leiva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7248)


We consider the problem of selecting investment components according to two partially opposed measures: the portfolio performance and its risk. We approach this within Markowitz’s model, considering the case of mutual funds market in Europe until July 2010. Comparisons were made on three multi-objective evolutionary algorithms, namely NSGA-II, SPEA2 and IBEA. Two well-known performance measures are considered for this purpose: hypervolume and R 2 indicator. The comparative analysis also includes an assessment of the financial efficiency of the investment portfolio selected according to Sharpe’s index, which is a measure of performance/risk. The experimental results hint at the superiority of the indicator-based evolutionary algorithm.


Genetic Algorithm Pareto Front Mutual Fund Portfolio Optimization Investment Portfolio 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Feijoo E. Colomine Duran
    • 1
  • Carlos Cotta
    • 2
  • Antonio J. Fernández-Leiva
    • 2
  1. 1.Laboratorio de Computación de Alto Rendimiento (LCAR)Universidad Nacional Experimental del Táchira (UNET)San CristóbalVenezuela
  2. 2.Dept. Lenguajes y Ciencias de la Computación, ETSI InformáticaUniversity of MálagaMálagaSpain

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