Abstract
The parameter setting of an algorithm that will result in optimal performance is a tedious task for users who spend a lot of time fine-tuning algorithms for their specific problem domains. This paper presents a multi-agent tuning system as a framework to set the parameters of a given algorithm which solves a specific problem. Besides, such a configuration is generated taking into account the current problem instance to be solved. We empirically evaluate our multi-agent tuning system using the configuration of a genetic algorithm applied to the root identification problem. The experimental results show the validity of the proposed model.
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References
Akaike, H.: A new look at statistical model identification. IEEE Transactions on Automatic Control 19, 716–723 (1974)
Barreiro, E.: Modelización y optimización de algoritmos genéticos para la selección de la solución deseada en resolución construtiva de restricciones geométricas. PhD thesis, Dpto. de Informática. Universidade de Vigo., Marzo (2006)
Bouma, W., Fudos, I., Hoffmann, C., Cai, J., Paige, R.: Geometric constraint solver. Computer-Aided Design 27(6), 487–501 (1995)
DeGroot, M.H.: Optimal Statistical Decisions. McGraw-Hill, New York (1970)
Essert-Villard, C., Schreck, P., Dufourd, J.F.: Sketch-based pruning of a solution space within a formal geometric constraint solver. Artificial Intelligence Journal 1(124), 139–159 (2000)
Document Title Fipa. Fipa interaction protocol library specification, http://www.fipa.org/specs/fipa00025/
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Heckerman, D.: A tutorial on learning with bayesian networks. Technical Report MSR-TR-95-06, Microsoft Research, Redmond, Washington (1995)
Hoffmann, C.M., Joan-Arinyo, R.: A brief on constraint solving. Computer-Aided Design and Applications 2(5), 655–663 (2005)
Jeffreys, H.: Theory of Probability. Oxford University Press, Oxford (1983)
Jensen, F.V.: An Introduction to Bayesian Networks. Springer, Heidelberg (1996)
Kohavi, R., John, G.: Automatic parameter selection by minimizing estimated error. In: Prieditis, A., Russell, S. (eds.) Proceedings of the 12th International Conference on Machine Learning, Lake Tahoe, CA, pp. 304–312. Morgan Kaufmann, San Francisco (1995)
Luzón, M.V.: Resolución de Restricciones geométricas. Selección de la Solución Deseada. PhD thesis, Dpto. de Informática. Universidade de Vigo., Septiembre (2001)
Nilsson, D.: An efficient algorithm for finding the m most probable configurations in bayesian networks. Statistics and Computing 8(2), 159–173 (1998)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1998)
Sagrado, J.D.: Fusión topológica y cuantitativa de redes causales. Universidad de Almería. Servicio de publicaciones (2000)
Schwefel, H.P.: Evolution and Optimum Seeking. John Wiley and Sons, Chichester (1995)
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction and Search. Lectures Notes in Statistics, vol. 81. Springer, New York (1993)
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Pavón, R., Glez-Peña, D., Laza, R., Díaz, F., Luzón, M.V. (2009). A Multi-Agent System Approach for Algorithm Parameter Tuning. In: Demazeau, Y., Pavón, J., Corchado, J.M., Bajo, J. (eds) 7th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2009). Advances in Intelligent and Soft Computing, vol 55. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00487-2_15
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DOI: https://doi.org/10.1007/978-3-642-00487-2_15
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