Commenced in January 2007
Paper Count: 30067
Statistical Genetic Algorithm
Abstract:Adaptive Genetic Algorithms extend the Standard Gas to use dynamic procedures to apply evolutionary operators such as crossover, mutation and selection. In this paper, we try to propose a new adaptive genetic algorithm, which is based on the statistical information of the population as a guideline to tune its crossover, selection and mutation operators. This algorithms is called Statistical Genetic Algorithm and is compared with traditional GA in some benchmark problems.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330823Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF
 A. Hamzeh, A. Rahmani (2004), Adaptive Crossover in Genetic Algorithms using Pattern Based Method. In proceeding of 9-Th Computer Society of Iran Computer Conference.
 A. Hamzeh, A. Rahmani (2005), A New Selection Method for Genetic Algorithms based on Genotypic Information of the Population. In proceedings of 10-The Computer Society of Iran Computer Conference.
 S. Yang (2002), Adaptive Crossover in Genetic Algorithms Using Statistics Mechanism. In Artificial Life VIII, Standish, Abbass, Bedau (eds)(MIT Press) 2002. pp 182-185.
 H. Luchian, O. Gheorghies (2003), Integrated-Adaptive Genetic Algorithms, In Proceeding of 7th European Conference on Artificial Life (ECAL 2003), Dortmund, Germany, September 14-17, 2003.
 J. G├│mez, D. Dasgupta, F.A. Gonz├ílez (2003), Using Adaptive Operators in Genetic Search. In Proceeding of GECCO 2003: 1580- 1581.
 J. Gomez, D. Dasgupta (2002), Using Competitive Operators and a Local Selection Scheme in Genetic Search. In Late-breaking papers GECCO 2002, 2002.
 F. Herrera, M. Lozano (2003), Fuzzy adaptive genetic algorithms: design, taxonomy, and future directions, In Journal of Soft Computing 7 (2003) 545-562, Springer-Verlag 2003.
 Y. Maeda, Q. Li (2005), Parallel Genetic Algorithm with Adaptive Genetic Parameters Tuned by Fuzzy Reasoning, International Journal of Innovative Computing, Information and Control Volume 1, Number 1, March 2005 pp 95-107.
 M. Mitchell (1996). An Introduction to Genetic Algorithms, The MIT Press, Cambridge, Massachusetts.
 S. Forrest, M. Mitchell (1993), What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation, In Machine Learning Journal, Volume 13, Issue 2-3 Nov. /Dec. Special issue on genetic algorithms pp: 285-319.
 T.Jones, S.Forrest (1995), Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In Larry Eshelman, editor, Proceedings of the Sixth International Conference on Genetic Algorithms, pages 184-192, San Francisco, CA.