A portfolio optimization approach to selection in multiobjective evolutionary algorithms

Date

2014-08

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

Springer

Type

Article

Peer reviewed

Yes

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.

Description

Keywords

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

Citation

Yevseyeva I., Guerreiro A.P., Emmerich M.T.M., Fonseca C.M. (2014) A portfolio optimization approach to selection in multiobjective evolutionary algorithms. In: T. Bartz-Beielstein, J. Branke, B. Filipič, J. Smith (Eds.) Parallel Problem Solving from Nature – PPSN XIII. Ser. LNCS (vol. 8672), Springer, 2014, pp. 672-681

Rights

Research Institute

Cyber Technology Institute (CTI)