Abstract
Autonomous Search is a modern technique aimed at introducing self-adjusting features to problem-solvers. In the context of constraint satisfaction, the idea is to let the solver engine to autonomously replace its solving strategies by more promising ones when poor performances are identified. The replacement is controlled by a choice function, which takes decisions based on information collected during solving time. However, the design of choice functions can be done in very different ways, leading of course to very different resolution processes. In this paper, we present a performance evaluation of 16 rigorously designed choice functions. Our goal is to provide new and interesting knowledge about the behavior of such functions in autonomous search architectures. To this end, we employ a set of well-known benchmarks that share general features that may be present on most constraint satisfaction and optimization problems. We believe this information will be useful in order to design better autonomous search systems for constraint satisfaction.
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References
Crawford, B., Soto, R., Castro, C., Monfroy, E.: A hyperheuristic approach for dynamic enumeration strategy selection in constraint satisfaction. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds.) IWINAC 2011, Part II. LNCS, vol. 6687, pp. 295–304. Springer, Heidelberg (2011)
Crawford, B., Castro, C., Monfroy, E., Soto, R., Palma, W., Paredes, F.: Dynamic selection of enumeration strategies for solving constraint satisfaction problems. Rom. J. Inf. Sci. Tech. 15, 106–128 (2012)
Crawford, B., Soto, R., Castro, C., Monfroy, E., Paredes, F.: An extensible autonomous search framework for constraint programming. Int. J. Phys. Sci. 6(14), 3369–3376 (2011)
Hamadi, Y., Monfroy, E., Saubion, F.: What is autonomous search? In: van Hentenryck, P., Milano, M. (eds.) Hybrid Optimization: The Ten Years of CPAIOR. Springer, Heidelberg (2011)
Hamadi, Y., Monfroy, E., Saubion, F. (eds.): Autonomous Search. Springer Science + Business Media, Heidelberg (2012)
Hatamlou, A.: Black hole: a new heuristic optimization approach for data clustering. Inf. Sci. 222, 175–184 (2013)
Kumar, S., Datta, D., Singh, S.: Black hole algorithm and its applications. In: Azar, A.T., Vaidyanathan, S. (eds.) Computational Intelligence Applications in Modeling and Control. Studies in Computational Intelligence, vol. 575, pp. 147–170. Springer International Publishing, Heidelberg (2015)
Soto, R., Crawford, B., Misra, S., Palma, W., Monfroy, E., Castro, C., Paredes, F.: Choice functions for autonomous search in constraint programming: Ga vs pso. Tech. Gaz. 20(4), 621–629 (2013)
Acknowledgments
Ricardo Soto is supported by Grant CONICYT / FONDECYT / REGULAR / 1160455, Broderick Crawford is supported by Grant CONICYT / FONDECYT / REGULAR / 1140897 and Rodrigo Olivares is supported by Postgraduate Grant Pontificia Universidad Católica de Valparaíso 2016.
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Soto, R., Crawford, B., Olivares, R., Niklander, S., Olguín, E. (2016). Autonomous Search in Constraint Satisfaction via Black Hole: A Performance Evaluation Using Different Choice Functions. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_6
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DOI: https://doi.org/10.1007/978-3-319-41000-5_6
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