The mean-variance cardinality constrained portfolio optimization problem: An experimental evaluation of five multiobjective evolutionary algorithms

Abstract

This paper compares the effectiveness of five state-of-the-art multiobjective evolutionary algorithms 
(MOEAs) together with a steady state evolutionary algorithm on the mean-variance cardinality constrained 
portfolio optimization problem (MVCCPO). The main computational challenges of the model are due to the 
presence of a nonlinear objective function and the discrete constraints. The MOEAs considered are the 
Niched Pareto genetic algorithm 2 (NPGA2), non-dominated sorting genetic algorithm II (NSGA-II), Pareto 
envelope-based selection algorithm (PESA), strength Pareto evolutionary algorithm 2 (SPEA2). and 
e-multiobjective evolutionary algorithm (e-MOEA). The computational comparison was performed using 
formal metrics proposed by the evolutionary multiobjective optimization community on publicly available 
data sets which contain up to 2196 assets. (C) 2011 Elsevier Ltd. All rights reserved.