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Effectiveness of iterative asset selection based on bordered hessian for portfolio optimization problems

This paper introduces a new initialization method of individuals for genetic algorithm (GA) in portfolio optimization problems. In our approach, first a set of assets, variables, composing the portfolio is selected, and then combination of real-valued weights of the portfolio is optimized by GA. In the asset selection, a pairwise asset selection which is an iterative greedy scheme based on the bordered Hessian is adopted. In most case of greedy manner, the initial point of search to build a complete solution has a big effect on its quality. In numerical experiments, we compare how the initial selection of asset has an impact on initialization of the population of GA, and evaluate the quality of whole composition of the portfolio obtained by GA.