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A Portfolio Optimization Approach to Selection in Multiobjective Evolutionary Algorithms
Iryna Yevseyeva1, Andreia P. Guerreiro2, Michael T.M. Emmerich3, and Carlos M. Fonseca2
1Centre for Cybercrime and Computer Security School of Computing Science, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK
2CISUC, Department of Informatics Engineering, University of Coimbra, Pólo II, Pinhal de Marrocos, 3030-290, Coimbra, Portugal
3Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands
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
LNCS 8672, p. 672 ff.
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© Springer International Publishing Switzerland 2014