Abstract
In this paper, a new mechanism for automatically obtaining some control parameter values for Genetic Algorithms is presented, which is independent of problem domain and size. This approach differs from the traditional methods which require knowing the problem domain first, and then knowing how to select the parameter values for solving specific problem instances. The proposed method uses a sample of problem instances, whose solution allows to characterize the problem and to obtain the parameter values. To test the method, a combinatorial optimization model for data-object allocation in the Web (known as DFAR) was solved using Genetic Algorithms. We show how the proposed mechanism allows to develop a set of mathematical expressions that relates the problem instance size to the control parameters of the algorithm. The expressions are then used, in on-line process, to control the parameter values. We show the last experimental results with the self-tuning mechanism applied to solve a sample of random instances that simulates a typical Web workload. We consider that the proposed method principles must be extended to the self-tuning of control parameters for other heuristic algorithms.
This research was supported in part by CONACYT and COSNET.
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References
Fogel, D., Ghozeil, A.: Using Fitness Distributions to Design More Efficient Evolutionary Computations. In: Proceedings of the 1996 IEEE Conference on Evolutionary Computation, Nagoya, Japan, pp. 11–19. IEEE Press, Piscataway (1996)
Pérez, J., Pazos, R.A., Velez, L., Rodriguez, G.: Automatic Generation of Control Parameters for the Threshold Accepting Algorithm. In: Coello Coello, C.A., de Albornoz, Á., Sucar, L.E., Battistutti, O.C. (eds.) MICAI 2002. LNCS (LNAI), vol. 2313, pp. 119–127. Springer, Heidelberg (2002)
Back, T., Schwefel, H.P.: Evolution Strategies I: Variants and their computational implementation. In: Winter, G., Périaux, J., Galán, M., Cuesta, P. (eds.) Genetic Algorithms in Engineering and Computer Science, vol. 6, pp. 111–126. John Wiley and Sons, Chichester (1995)
Mercer, R.E., Sampson, J.R.: Adaptive Search Using a Reproductive Meta-plan. Kybernets 7, 215–228 (1978)
Grefenstette, J.J.: Optimization of Control Parameters for Genetic Algorithms. In: Sage, A.P. (ed.) IEEE Transactions on Systems, Man and Cybernetics, vol. SMC-16(1), pp. 122–128. IEEE, New York (1986)
Smith, R.E., Smuda, E.: Adaptively Resizing Population: Algorithm Analysis and First Results. Complex Systems 9, 47–72 (1995)
Harik, G.R., Lobo, F.G.: A parameter-less Genetic Algorithm. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference GECCO 1999, pp. 258–267. Morgan Kaufmann, San Francisco (1999)
Pérez, J., Pazos, R.A., Romero, D., Santaolaya, R., Rodríguez, G., Sosa, V.: Adaptive and Scalable Allocation of Data-Objects in the Web. In: Kumar, V., Gavrilova, M.L., Tan, C.J.K., L’Ecuyer, P. (eds.) ICCSA 2003. LNCS, vol. 2667, pp. 134–143. Springer, Heidelberg (2003)
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Pérez O., J. et al. (2004). Dynamic Allocation of Data-Objects in the Web, Using Self-tuning Genetic Algorithms. In: Bazzan, A.L.C., Labidi, S. (eds) Advances in Artificial Intelligence – SBIA 2004. SBIA 2004. Lecture Notes in Computer Science(), vol 3171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28645-5_38
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DOI: https://doi.org/10.1007/978-3-540-28645-5_38
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