| Abstract: |
| \textit{Optimal portfolio selection} -- composing a set of stocks/assets that provide high yields/returns with a reasonable risk -- has attracted investors and researchers for a long time. As a consequence, a variety of methods and techniques have been developed, spanning from purely mathematics ones to computational intelligence ones. In this paper, we introduce a method for optimal portfolio selection based on multi-objective evolutionary algorithms, specifically \textit{Nondominated Sorting Genetic Algorithm}-II (NSGA-II), which tries to \textit{maximize} the yield and \textit{minimize} the risk, simultaneously. The system, named \textit{EvoFolio}, has been experimented on stock datasets in a three-years time-frame and varying the configurations/specifics of NSGA-II operators. \textit{EvoFolio} is an \textit{interactive} genetic algorithm, i.e., users can provide their own insights and suggestions to the algorithm such that it takes into account users` preferences for some stocks. We have performed tests with optimizations occurring quarterly and monthly. The results show how \textit{EvoFolio} can significantly reduce the risk of portfolios consisting only of stocks and obtain very high performance (in terms of return). Furthermore, considering the investor`s preferences has proved to be very effective in the portfolio`s composition and made it more attractive for end-users. We argue that \textit{EvoFolio} can be effectively used by investors as a support tool for portfolio formation. |
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