Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Multiobjective robustness for portfolio optimization in volatile environments
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Hybrid Local Search for Constrained Financial Portfolio Selection Problems
CPAIOR '07 Proceedings of the 4th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A portfolio optimization model with three objectives and discrete variables
Computers and Operations Research
Robust optimization framework for cardinality constrained portfolio problem
Applied Soft Computing
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Dynamic index tracking via multi-objective evolutionary algorithm
Applied Soft Computing
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The portfolio optimization problem is a well-known difficult problem occurring in financial real world. The problem consists in choosing an optimal set of assets in order to minimize the risk and maximize the profit of the investment. A multiobjective approach to this problem is suggested in this paper. We use three well-known Evolutionary Algorithms (namely NSGA2, PESA and SPEA2) for solving the bi-objective portfolio optimization problem. Several numerical experiments are performed using real-world data. The results show that PESA outperforms NSGA2 and SPEA2 for the considered test cases.