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Reseach Article

Empirical Analysis and Random Respectful Recombination of Crossover and Mutation in Genetic Algorithms

Published on None 2010 by V.Kapoor, S.Dey, A.P.Khurana
Evolutionary Computation for Optimization Techniques
Foundation of Computer Science USA
ECOT - Number 1
None 2010
Authors: V.Kapoor, S.Dey, A.P.Khurana
f4521397-c325-424b-bbb5-878ad8610765

V.Kapoor, S.Dey, A.P.Khurana . Empirical Analysis and Random Respectful Recombination of Crossover and Mutation in Genetic Algorithms. Evolutionary Computation for Optimization Techniques. ECOT, 1 (None 2010), 25-30.

@article{
author = { V.Kapoor, S.Dey, A.P.Khurana },
title = { Empirical Analysis and Random Respectful Recombination of Crossover and Mutation in Genetic Algorithms },
journal = { Evolutionary Computation for Optimization Techniques },
issue_date = { None 2010 },
volume = { ECOT },
number = { 1 },
month = { None },
year = { 2010 },
issn = 0975-8887,
pages = { 25-30 },
numpages = 6,
url = { /specialissues/ecot/number1/1530-133/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Evolutionary Computation for Optimization Techniques
%A V.Kapoor
%A S.Dey
%A A.P.Khurana
%T Empirical Analysis and Random Respectful Recombination of Crossover and Mutation in Genetic Algorithms
%J Evolutionary Computation for Optimization Techniques
%@ 0975-8887
%V ECOT
%N 1
%P 25-30
%D 2010
%I International Journal of Computer Applications
Abstract

Genetic algorithms (GAs) are multi-dimensional, blind & heuristic search methods which involves complex interactions among parameters (such as population size, number of generations, various type of GA operators, operator probabilities, representation of decision variables etc.). Our belief is that GA is robust with respect to design changes. The question is whether the results obtained by GA depend upon the values given to these parameters is a matter of research interest. This paper studies the problem of how changes in the four GA parameters (population size, number of generations, crossover & mutation probabilities) have an effect on GA’s performance from a practical stand point. To examine the robustness of GA to control parameters, we have tested two groups of parameters & the interaction inside the group (a) Crossover & mutation alone (b) Crossover combined with mutation . Based on calculations and simulation results it is seen that for simple problems mutation plays an momentous role. For complex problems crossover is the key search operator. Based on our study complementary crossover & mutation probabilities is a reliable approach.

References
  1. Aguirre, H.E., Tanaka, K., 2002. Parallel varying mutation genetic algorithms. IEEE transactions.
  2. BOYABALTI, O., SABUNCUOGLU, I., 2007. Parameter Selection in Genetic Algorithms. System, Cybernatics & Informatics. Volume 2-Number 4, pp. 78-83.
  3. Cao, Y.J., Wu, Q.H., 1999. Optimization of control parameters in genetic algorithms: a stochastic approach. International journal of systems science, volume 30, number 2, pp. 551-559.
  4. Cicirello, V.A., Smith, S.F., 2000. Modelling GA Performance for Control Parameter Optimization. Proceedings of Genetic & Evolutionary Computing Conference. GEECO-2000.
  5. Culberson, J.C., 1994. Mutation-Crossover Isomorphisms and the construction of discriminating functions. Evolutionary Computation 2(3). 279-311.
  6. De Jong, K. A., Spears, W. M. 1990. 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.
  7. Eiben, A.E., Michalewich, Z., Schoenaur, M., Smith, J.E., 1999. parameter control in evolutionary algorithms. Proceedings of Genetic & Evolutionary Computing Conference.
  8. Goldberg, D.E., 1989a. Genetic algorithm in search, optimization & machine learning. New York: Addison Wisley.
  9. Goldberg, D.E., 1989b. 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.
  10. Goldberg, D.E., Deb, K., 1991. A comparative analysis of selection schemes used in genetic algorithms. In: Rawlins, Gregory J.E. (Ed.), Foundations of Genetic
  11. Goldberg, D.E., Deb, K., 1991. A comparitive analysis of selection schemes used in genetic algorithms. In : Rawlins, Gregory J.E. (Ed.), Foundations of Genetic algorithms. Morgan Kaufmann Publishers, Inc., pp. 69–93.
  12. Harik, R.G., Lobo.F.G., 1999. A parameter-less genetic algorithm. IEEE transactions on evolutionary computation.
  13. Hinterding, R., Gielewski, H., Peachey, T.C., 1995. Proceedings of the 5th International Conference on Genetic Algorithms.
  14. Jones, T., 1995. Crossover, macromutation, and population-based search. Proceedings of 6th International conference on genetic algorithms.
  15. Kargupta, H., Deb, K., Goldberg, D.E., 1999. Ordering genetic algorithms and deception. Parallel problem solving from nature 2. pp. 47-53.
  16. Muhlenbein, H., 1992. How genetic algorithms really work I. Mutation and Hillclimbing. Foundation of genetic algorithms II pp. 15-25.
  17. Radcliffe, N., 2002. Forma analysis and random respectful recombination. Proceedings of 4th International conference on genetic algorithms.
  18. Rana, S., 1999. The distributional baises of crossover operators. Proceedings of Genetic & Evolutionary Computing Conference.
  19. Spears, W. M. & De Jong, K. A. 1991. An analysis of multi-point crossover. Proceedings of the Fourth International Conference on Genetic Algorithms, 230-236. La Jolla, CA: Morgan Kaufmann.
  20. Spears, W. M. & De Jong, K. A. 1991. On the virtues of uniform crossover. Proceedings of the Fourth International Conference on Genetic Algorithms, 230-236. La Jolla, CA: Morgan Kaufmann.
  21. Spears, W. M., 1995. Adapting crossover in evolutionary algorithms. Proceedings of the Fourth International Conference on Evolutionary programming.
  22. Spears, W. M., 1994. Adapting crossover in a genetic algorithm. Artificial intelligence center internal report # AIC-94-019.
  23. Srinivas, M. & Patnaik, L.M., 1994. Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE transactions on Systems, Man & Cybernatics, Vol. 24, No. 4.
  24. Tate, D.M., Smith, A.E., 1993. Expected allele coverage and role of mutation in genetic algorithms. Proceedings of the 5th International Conference on Genetic Algorithms. pp. 31- 37.
Index Terms

Computer Science
Information Sciences

Keywords

Genetic algorithm control parameters crossover mutation population sizing