Autonomous search in constraint satisfaction via black hole: A performance evaluation using different choice functions
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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. © Springer International Publishing Switzerland 2016.
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Conference PaperSoto R.; Crawford B.; Olivares R.; Niklander S.; Olguín E. (Springer Verlag, 2016)
Evaluation of choice functions to self-adaptive on constraint programming via the black hole algorithm (2020) Olivares R.; Soto R.; Crawford B.; Barria M.; Niklander S. (Institute of Electrical and Electronics Engineers Inc., 2017)
Evaluating the efficient of using choice functions to solve CSPs via Autonomous Search [Evaluando La Eficiencia De Utilizar Funciones De Selección En Búsqueda Autónoma Para Resolver Problemas De Satisfacción De Restricciones] (2020) Soto R.; Crawford B.; Olivares R.; Olguin E. (IEEE Computer Society, 2016)