ACM Transactions on Mathematical Software (TOMS)
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Experimental study on a hybrid nature-inspired algorithm for financial portfolio optimization
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
Stock index tracking by Pareto efficient genetic algorithm
Applied Soft Computing
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In previous studies, nature-inspired algorithms have been implemented in order to tackle hard NP-optimization problems, in the financial domain. Specifically, the task of finding optimal combination of assets with the aim of efficiently allocating your available capital is of major concern. One of the main reasons, which justifies the difficulties entailed in this problem, is the high level of uncertainty in the financial markets and not only. As mentioned above, artificial intelligent algorithms may provide a solution to this task. However, there is one major drawback concerning these techniques: the large number of open parameters. The aim of this study is twofold. Firstly, results from extended simulations are presented regarding the application of a specific hybrid nature-inspired metaheuristic in a particular formulation of the financial portfolio optimization problem. The main focus is on presenting comparative results regarding the performance of the proposed scheme for various configuration settings. Secondly, it is our intend to enhance the hybrid scheme's performance by incorporating intelligent searching components such as other metaheuristics (simulated annealing).