Title :
The gene expression messy genetic algorithm for financial applications
Author :
Kargupta, Hillol ; Buescher, Kevin
Author_Institution :
Comput. Sci. Methods Div., Los Alamos Nat. Lab., NM, USA
Abstract :
The paper introduces the gene expression messy genetic algorithm (GEMGA)-a new generation of messy GAs that may find many applications in financial engineering. Unlike other existing blackbox optimization algorithms, GEMGA directly searches for relations among the members of the search space. The GEMGA is an O(|Λ|k(l+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 length L and alphabet set Λ. The GEMGA is designed based on the alternate perspective of natural evolution proposed by the SEARCH framework (Kargupta, 1995) that emphasizes the role of gene expression. The paper also presents the test results for large multimodal problems and identifies possible applications to financial engineering
Keywords :
computational complexity; financial data processing; genetic algorithms; search problems; SEARCH framework; blackbox optimization algorithms; complexity; financial engineering; gene expression; gene expression messy genetic algorithm; large multimodal problems; order-k delineable problems; search space; sequence representation; Algorithm design and analysis; Art; Gene expression; Genetic algorithms; Genetic engineering; Laboratories; Optimized production technology; Predictive models; System identification; Testing;
Conference_Titel :
Computational Intelligence for Financial Engineering, 1996., Proceedings of the IEEE/IAFE 1996 Conference on
Conference_Location :
New York City, NY
Print_ISBN :
0-7803-3236-9
DOI :
10.1109/CIFER.1996.501840
