Hybrid evolutionary algorithms for a multiobjective financial problem

We examine the use of numeric score functions that allow one to rank order a universe of stocks based on profitability. We use a genetic algorithm to evolve sets of `implicit-positive´ binary classification rules. Using each rule set, we induce a scoring model by weighting the individual terms in a representation of the rule in terms of binary variables. We report on the empirical performance of the proposed family of scoring algorithms on several large historical stock data sets. We also compare our approach with a polynomial network technique