A Portfolio Optimization Approach to Selection in Multiobjective Evolutionary Algorithms

  • Iryna Yevseyeva
  • Andreia P. Guerreiro
  • Michael T. M. Emmerich
  • Carlos M. Fonseca
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8672)


In this work, a new approach to selection in multiobjective evolutionary algorithms (MOEAs) is proposed. It is based on the portfolio selection problem, which is well known in financial management. The idea of optimizing a portfolio of investments according to both expected return and risk is transferred to evolutionary selection, and fitness assignment is reinterpreted as the allocation of capital to the individuals in the population, while taking into account both individual quality and population diversity. The resulting selection procedure, which unifies parental and environmental selection, is instantiated by defining a suitable notion of (random) return for multiobjective optimization. Preliminary experiments on multiobjective multidimensional knapsack problem instances show that such a procedure is able to preserve diversity while promoting convergence towards the Pareto-optimal front.


Fitness assignment portfolio selection Sharpe ratio evolutionary algorithms multiobjective knapsack problem 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Iryna Yevseyeva
    • 1
  • Andreia P. Guerreiro
    • 2
  • Michael T. M. Emmerich
    • 3
  • Carlos M. Fonseca
    • 2
  1. 1.Centre for Cybercrime and Computer Security, School of Computing ScienceNewcastle UniversityNewcastle upon TyneUK
  2. 2.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal
  3. 3.Leiden Institute of Advanced Computer ScienceLeiden UniversityLeidenThe Netherlands

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