A Portfolio Optimization Approach to Selection in Multiobjective Evolutionary Algorithms
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.
KeywordsFitness assignment portfolio selection Sharpe ratio evolutionary algorithms multiobjective knapsack problem
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- 2.Baker, J.E.: Reducing bias and inefficiency in the selection algorithm. In: Proc. Second International Conference on Genetic Algorithms, pp. 14–21 (1987)Google Scholar
- 3.Baker, J.E.: Adaptive selection methods for genetic algorithms. In: Proc. First International Conference on Genetic Algorithms, pp. 101–111 (1985)Google Scholar
- 5.Cornuejols, G., Tutuncu, R.: Optimization Methods in Finance. Cambridge University Press (2007)Google Scholar
- 9.Guerreiro, A.P.: Portfolio Selection in Evolutionary Algorithms. Ph.D. thesis proposal, University of Coimbra, Coimbra, Portugal (2012)Google Scholar
- 12.Knowles, J., Corne, D., Fleisher, M.: Bounded archiving using the Lebesgue measure. In: Proc. IEEE Congress on Evolutionary Computation (CEC 2003), vol. 4, pp. 2490–2497. IEEE Press, New York (2003)Google Scholar
- 13.Le, K., Landa-Silva, D.: Obtaining better non-dominated sets using volume dominance. In: IEEE Congress on Evolutionary Computation (CEC 2007), pp. 3119–3126 (September 2007)Google Scholar
- 16.Markowitz, H.: Portfolio selection. Journal of Finance 7(1), 77–91 (1952)Google Scholar
- 18.Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm. In: Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems (EUROGEN 2001), pp. 95–100 (2002)Google Scholar