Multi-population Genetic Algorithm for Cardinality Constrained Portfolio Selection Problems

Portfolio Selection (PS) is recognized as one of the most important and challenging problems in financial engineering. The aim of PS is to distribute a given amount of investment fund across a set of assets in such a way that the return is maximised and the risk is minimised. To solve PS more effectively and more efficiently, this paper introduces a Multi-population Genetic Algorithm (MPGA) methodology. The proposed MPGA decomposes a large population into multiple populations to explore and exploit the search space simultaneously. These populations evolve independently during the evolutionary learning process. Yet different populations periodically exchange their individuals so promising genetic materials could be shared between different populations. The proposed MPGA method was evaluated on the standard PS benchmark instances. The experimental results show that MPGA can find better investment strategies in comparison with state-of-the-art portfolio selection methods. In addition, the search process of MPGA is more efficient than these existing methods requiring significantly less amount of computation.