The gene expression messy genetic algorithm for financial applications

This paper introduces the gene expression messy genetic algorithm (GEMGA)|a new generation of messy GAs that may nd many applications in nan-cial engineering. Unlike other existing blackbox optimization algorithms, GEMGA directly searches for relations among the members of the search space. The GEMGA is an O(jj k (` + k)) sample complexity algorithm for the class of order-k delineable problems (Kargupta, 1995) (problems that can be solved by considering no higher than order-k relations) in sequence representation of lengthànd alphabet set. The GEMGA is designed based on alternate perspective of natural evolution proposed by the SEARCH framework (Kargupta, 1995) that emphasizes the role of gene expression. This paper also presents the test results for large multimodal problems and identiies possible applications to nancial engineering.