Abstract
Genetic Algorithms use different parameters to control their evolutionary search for the solution to problems. However, there are no standard rules for choosing the best parameter values, being difficult to know whether the parameter values must be fixed during a run or must be modified dynamically. Besides, there are many theoretical results on parameter control, but however, very often real world problems call for shortcuts and/or some ad hoc solutions. This paper presents an effective approach for optimization of control parameters which is based on a meta-GA combined with an adaptation strategy to improve the GA performance. In order to validate the approach, it has been applied to verify the performance of a real system: a computer network. The results have been compared with the ones obtained for other methods: using fixed and adapted parameter values. A statistical analysis has been done to ascertain whether differences are significant.
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References
Goldberg, D.E.: Genetic Algorithms in search, optimization and Machine Learning. Addison-Wesley, New York (1989)
Cordón, O., Herrera, F., Hoffmann, F., Magdalena, L.: Genetic Fuzzy Systems: Evolutionary tuning and learning of fuzzy knowledge bases. Advances in fuzzy systems – Applications and theory, vol. 19. World Scientific Publishing, Singapore (2001)
Herrera, F., Lozano, M.: Fuzzy adaptive genetic algorithms: design, taxonomy, and future directions. Soft Computing 7, 545–562 (2003)
Grefenstette, J.J.: Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics 16(1), 122–128 (1986)
Eiben, A.E., Michalewicz, Z., Schoenauer, M., Smith, J.E.: Parameter Control in Evolutionary Algorithms. In: Parameter Setting in Evolutionary Algorithms. Springer, Heidelberg (2007)
De Jong, K.: An analysis of the behaviour of a class of genetic adaptive systems. PhD thesis, University of Michigan (1975)
Grefenstette, J.J.: Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics 16(1), 122–128 (1986)
Bramlette, M.F.: Initialization, mutation and selection methods in genetic algorithms for function optimization. In: Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 100–107. Morgan Kaufmann, San Mateo (1991)
Eiben, A.E., Hinterding, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation 3, 124–141 (1999)
Cicirello, V.A., Smith, S.F.: Modeling GA performance for control parameter optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 235–242. Morgan Kaufmann Publishers, Las Vegas (2000)
Michalewicz, Z., Schmidt, M.: Parameter Control in Practice. In: Parameter Setting in Evolutionary Algorithms. Springer, Heidelberg (2007)
Wolpert, D.H., MacReady, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67–82 (1997)
Hinterding, R., Michalewicz, Z., Eiben, G.: Adaptation in Evolutionary Computation: A Survey. In: Proc. of the IEEE Conference on Evolutionary Computation, pp. 65–69 (1997)
De Jong, K.: Parameter Setting in EAs: a 30 year Perspective. Parameter Setting in Evolutionary Algorithms. Springer, Heidelberg (2007)
Hui, J.: Switching and Traffic Theory for Integrated Broad Band Networks. Kluwer Academic Publisher, Dordrecht (1990)
Karagiannis, T., Molle, M., Faloutsos, Broido, A.: A nonstationary poisson view of internet traffic. Proc. of the Twenty-third Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM), Hong Kong, vol. 3, pp. 1558–1569 (2004)
Cao, J., Cleveland, W., Lin, D., Sun, D.: Internet traffic Tends Toward Poisson and Independent as the Load Increases. In: Holmes, C., Dennison, D., Hansen, M., Yu, B., Mallick, B. (eds.) Nonlinear Estimation and Classification 2002. LNS, pp. 83–109. Springer, Heidelberg (2003)
Corne, D.W., Oates, M.J., Smith, G.D.: Telecommunications Optimization: Heuristic and Adaptive Techniques. John Wiley and Sons Ltd., Chichester (2000)
Karthik, S., Jawajar, V., Chidambararajan, B., Srivatsa, S.K.: Performance of TCP over satellite networks under severe cross-traffic using GA. International Journal Mobile Communications 2(4), 382–394 (2004)
The Network Simulator -ns-2, http://www.isi.edu/nsnam/ns
Fernández-Prieto, J.A., Velasco, J.R.: Application of Genetic Algorithms in the research of the optimum probabilities of the genetic operators. In: 8th International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems (IPMU), pp. 291–297 (2000)
GAlib, http://lancet.mit.edu/ga
Herrera, F., Lozano, M., Verdegay, J.L.: The Use of Fuzzy Connectives to Design Real-Coded Genetic Algorithms. Mathware and Soft Computing 1(3), 239–251 (1995)
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Fernández-Prieto, J.A., Canada-Bago, J., Gadeo-Martos, M.A., Velasco, J.R. (2010). A Case Study of Parameter Control in a Genetic Algorithm: Computer Network Performance. In: Chatzimisios, P., Verikoukis, C., Santamaría, I., Laddomada, M., Hoffmann, O. (eds) Mobile Lightweight Wireless Systems. Mobilight 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 45. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16644-0_56
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DOI: https://doi.org/10.1007/978-3-642-16644-0_56
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