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.


Parallel Problem Solving from Nature – PPSN XIII, 13th International Conference, Ljubljana, Slovenia, September 13-17, 2014. Proceedings, LNCS 8672 2014


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Year 2016 : 1 citations

 Ahmadi, Aras. "Memory-based Adaptive Partitioning (MAP) of search space for the enhancement of convergence in Pareto-based multi-objective evolutionary algorithms." Applied Soft Computing (2016).

Year 2015 : 1 citations

 Souravlias, D., K. E. Parsopoulos, and I. S. Kotsireas. "Circulant weighing matrices: a demanding challenge for parallel optimization metaheuristics." Optimization Letters (2015): 1-12.