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Reseach Article
Article:An Empirical Study of the Role of Control Parameters of Genetic Algorithms in Function Optimization Problems
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 31 - Number 6 |
| Year of Publication: 2011 |
| Authors: V.Kapoor, S.Dey, A.P.Khurana |
10.5120/3828-5319
|
V.Kapoor, S.Dey, A.P.Khurana . Article:An Empirical Study of the Role of Control Parameters of Genetic Algorithms in Function Optimization Problems. International Journal of Computer Applications. 31, 6 ( October 2011), 20-26. DOI=10.5120/3828-5319
Abstract
Genetic algorithms (GAs) are multi-dimensional, blind heuristic search methods that involve complex interactions among parameters (such as population size, number of generations, GA operators and operator probabilities). The question whether the quality of results obtained by GAs depend upon the values given to these parameters, is a matter of research interest. This work studies the problem of how changes in four GA parameters (population size, number of generations, crossover and mutation probabilities) affect GA performance from a practical stand point. To examine the robustness of GA to these parameters, we have tested three groups of parameters and the interactions in each group (a) Crossover and mutation separately (b) Crossover combined with mutation together (c) Population size and number of generations. The results show that for simple problems mutation plays a momentous role, and for complex problems crossover is the key search operator. Based on our study we conclude that, complementary crossover and mutation probabilities combined with a reasonable population size is a reliable approach.
References
- D. E. Goldberg, K. Deb, “A comparative analysis of selection schemes used in genetic algorithm,” In: Rawlins, Gregory J.E. (Ed.), Foundations of Genetic Algorithms. Morgan Kaufmann Publishers, Inc., pp. 69–93, 1991.
- R. G. Harik, F. G. Lobo, “A parameter-less genetic algorithm,” IEEE transactions on evolutionary computation. 1999. [Online]. Available: http://w3.ualg.pt/~flobo/papers/plga-gecco99.pdf
- O. Boyabalti, I. Sabuncuoglu, “Parameter selection in genetic algorithms” System, Cybernatics & Informatics. Volume 2-Number 4, pp. 78-83, 2007. [Online]. Available: http://www.iiisci.org/journal/CV$/sci/pdfs/P409090.pdf
- V. A. Cicirello, S. F. Smith, “Modelling GA Performance for Control Parameter Optimization,” Proceedings of Genetic & Evolutionary Computing Conference. GEECO-2000. [Online]. Available: http://.ri.cmu.edu/publication_view.html?pub_id=3329
- Y. J. Cao, Q. H. Wu, “Optimization of control parameters in genetic algorithms: a stochastic approach,” International journal of systems science, volume 30, number 2, pp. 551-559, 1999. [Online]. Available: http://www.informaworld.com/smpp/content~db=all~content=a713866076~frm=abslink
- A. E. Eiben, Z. Michalewich, M. Schoenaur, J. E. Smith, “Parameter control in evolutionary algorithms,” Proceedings of Genetic & Evolutionary Computing Conference, 1999.
- N. Radcliffe, “Forma analysis and random respectful recombination,” Proceedings of 4th International conference on genetic algorithms, 1999.
- H. Kargupta, K. Deb, D. E. Goldberg, “Ordering genetic algorithms and deception,” Parallel problem solving from nature 2. pp. 47-53, 1992. [Online]. Available: http://www.illigal.uiuc.edu/pub/papers/Publication
- J. C. Culberson, “Mutation-Crossover Isomorphism’s and the construction of discriminating functions,” Evolutionary Computation 2(3). 279-311, 1994.
- D. E. Goldberg. Genetic algorithm in search, optimization & machine learning. New York: Addison Wisley, 1989.
- W. M. Spears, K. A. De Jong, “On the virtues of uniform crossover,” Proceedings of the Fourth International Conference on Genetic Algorithms, 230-236. La Jolla, CA: Morgan Kaufmann, 1991.
- W. M. Spears, K. A. De Jong, “An analysis of multi-point crossover,” Proceedings of the Fourth International Conference on Genetic Algorithms, 230-236. La Jolla, CA: Morgan Kaufmann, 1993.
- D. E. Goldberg, "Sizing populations for serial and parallel genetic algorithms,” In: Schaffer, J.D. (Ed), Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann, Los Altos, CA, pp. 70–79, 1989.
- H. Kargupta, K. Deb, D. E. Goldberg, “Ordering genetic algorithms and deception,” Parallel problem solving from nature 2. pp. 47-53, 1999.
- S. Rana, “The distributional baises of crossover operators,” Proceedings of Genetic & Evolutionary Computing Conference, 1999.
- K. A. De Jong, , W. M. Spears, “An analysis of the interacting roles of population size and crossover in genetic algorithms,” Proceedings of the International Conference on parallel problem solving from nature. Springer. pp. 38-47, 1990.
- W. M. Spears, “Adapting crossover in evolutionary algorithms,” Proceedings of the Fourth International Conference on Evolutionary programming, 1995. [Online]. Available: http://www.cs.uwyo.edu/~wspears/papers/ep95.pdf
- W. M. Spears, “Adapting crossover in a genetic algorithm,” Artificial intelligence center internal report # AIC-94-019, 1995. [Online]. Available: http://www.cs.uwyo.edu/~wspears/papers/adapt.cross
- H. Muhlenbein, “How genetic algorithms really work I. Mutation and Hillclimbing,” Foundation of genetic algorithms II pp. 15-25, 1992. [Online]. Available: http://muehlenbein.org/mut92.pdf
- D. M. Tate, A. E. Smith, “Expected allele coverage and role of mutation in genetic algorithms,” Proceedings of the 5th International Conference on Genetic Algorithms, pp. 31- 37, 1993.
- M. Srinivas, L. M. Patnaik, “Adaptive probabilities of crossover and mutation in genetic algorithms,” IEEE transactions on Systems, Man & Cybernatics, Vol. 24, No. 4, 1994. [Online]. Available: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=286385&tag=1
- R. Hinterding, H. Gielewski, T. C. .Peachey, “The nature of Mutation in Genetic Algorithms,” Proceedings of the 5th International Conference on Genetic Algorithms, 1995.
- M. D. Vose, “A closer look at mutation in genetic algorithms,” Annals of Mathematics and Artificial Intelligence, Vol. 10, No. 4, pp 423-434, 1994.
- H. E. Aguirre, K. Tanaka, “Parallel varying mutation genetic algorithms,” IEEE transactions, 2002.
- K. Deb. Optimization for Engineering Design. Algorithms and Examples. Prentice Hall of India. New Delhi, 2000.
- V. Kapoor, S. Dey, A. P. Khurana, “Empirical analysis and random respectful recombination of crossover and mutation in genetic algorithms,” International Journal of Computer Applications. Special issue on Evolutionary Computation for Optimization. ECOT, 2010. pp. 5-30, 2010. [Online]. Avaliable: http://www.ijcaonline.org/specialissues/ecot/number1/1530-133.
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