Abstract
Dealing with ill-defined optimization problems, where the actual values of the input parameters are unknown or not directly measurable, is generally not an easy task. In order to enhance the robustness of the final solutions, we propose in the current paper a hybrid metaheuristic approach that incorporates a sampling-based simulation module. Empirical application to the classical mean-variance portfolio optimization problem, which is known to be extremely sensitive to noises in asset means, is provided through a genetic algorithm solver. Results of the proposed approach are compared with that specified by the baseline worst-case scenario and the two approaches of stochastic programming and robust optimization.
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Rifki, O., Ono, H. (2017). A Hybrid Approach of Optimization and Sampling for Robust Portfolio Selection. In: Dörner, K., Ljubic, I., Pflug, G., Tragler, G. (eds) Operations Research Proceedings 2015. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-42902-1_43
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DOI: https://doi.org/10.1007/978-3-319-42902-1_43
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