Mathematical Study OF Adaptive Genetic Algorithm (AGA) with Mutation and Crossover probabilities
Keywords:
crossover probability (Pc ), mutation probability(Pm)Abstract
GAs is powerful search techniques that are used successfully to solve problems in many different disciplines.AGA are particularly easy to implement and promise substantial gains in performance. AGA has some parameters, such as population size, the crossover probability (Pc), the mutation probability (Pm) are varied while genetic algorithm is running. Genetic algorithm includes these parameters that should be adjusting so that the algorithm can provide positive results. The main aim of this paper is that how to design of adaptive crossover probability (Pc) and mutation probability (Pm).By varying Pc and Pm adaptively it response to the fitness values of the solution.
Depending on the fitness value of the solution, in AGA the crossover probability (Pc ) and the mutation probability(Pm) are varied. High- fitness solutions are „protected‟, while solutions with sub average fatnesses are totally disrupted, that is by varying the crossover probability (Pc ) and the mutation probability(Pm) adaptively in response to the fitness value of the solution: when the population tends to get struck at a local optimum, the crossover probability (Pc ) and the mutation probability(Pm) are increased and when the population is scattered in the solution space, the crossover probability (Pc ) and the mutation probability(Pm) are decreased.
References
. H. Aytug and G. J. Koehler, “Stopping criteria for finite length genetic algorithms, “INFORMS Journal on Computing, Vol. 8, 1996, pp. 183-191.
. T. Bäck, “Self-adaptation in genetic algorithms,” in Proceedings of the First European Conference on Artificial Life, 1992, pp. 263-271.
. T. Bäck, “Optimal mutation rates in genetic search,” in Proceedings of the Fifth International Conference on Genetic Algorithms, 1993, pp. 2-8.
. K. Deb and S. Argrawal, “Understanding interactions among genetic algorithm parameters,”in Foundations of Genetic Algorithms 5, 1998, pp. 265-286.
. K. A. D. Jong, “An analysis of the behavior of a class of genetic adaptive systems,” PhD thesis, University of Michigan, 1975.
. K. A. D. Jong, “Adaptive system design: A genetic approach,”IEEE Transactions on System, Man and Cybernetics, Vol. 10, 1980, pp. 566-574.
. A. E. Eiben, R. Hinterding, and Z. Michalewicz, “Parameter control in evolutionary algorithms,”IEEE Transactions on Evolutionary Computation, Vol. 3, 1999, pp.124-141.
. T. C. Fogarty, “Varying the probability of mutation in genetic algorithms,” in Proceedings of the Third International Conference on Genetic Algorithms, 1989, pp.104-109.
. D. E. Goldberg,Genetic Algorithms in Search, Optimization & Machine Learning,Addison Wesley, 1989.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2014 COMPUSOFT: An International Journal of Advanced Computer Technology
![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution 4.0 International License.
©2023. COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY by COMPUSOFT PUBLICATION is licensed under a Creative Commons Attribution 4.0 International License. Based on a work at COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY. Permissions beyond the scope of this license may be available at Creative Commons Attribution 4.0 International Public License.