Statistical Genetic Algorithm

Authors: Mohammad Ali Tabarzad, Caro Lucas, Ali Hamzeh

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.

Keywords: Genetic Algorithms, Statistical Information ofthe Population, PAUX, SSO.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330823

References:

[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] J. Gómez, D. Dasgupta, F.A. González (2003), Using Adaptive Operators in Genetic Search. In Proceeding of GECCO 2003: 1580- 1581.
[6] J. Gomez, D. Dasgupta (2002), Using Competitive Operators and a Local Selection Scheme in Genetic Search. In Late-breaking papers GECCO 2002, 2002.
[7] 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.
[8] 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.
[9] M. Mitchell (1996). An Introduction to Genetic Algorithms, The MIT Press, Cambridge, Massachusetts.
[10] 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.
[11] 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.