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A Portfolio Optimization Approach to Selection in Multiobjective Evolutionary Algorithms

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 8672)

Abstract

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.

Keywords

  • Fitness assignment
  • portfolio selection
  • Sharpe ratio
  • evolutionary algorithms
  • multiobjective knapsack problem

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Yevseyeva, I., Guerreiro, A.P., Emmerich, M.T.M., Fonseca, C.M. (2014). A Portfolio Optimization Approach to Selection in Multiobjective Evolutionary Algorithms. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds) Parallel Problem Solving from Nature – PPSN XIII. PPSN 2014. Lecture Notes in Computer Science, vol 8672. Springer, Cham. https://doi.org/10.1007/978-3-319-10762-2_66

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  • DOI: https://doi.org/10.1007/978-3-319-10762-2_66

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10761-5

  • Online ISBN: 978-3-319-10762-2

  • eBook Packages: Computer ScienceComputer Science (R0)