Evaluation of choice functions to self-adaptive on constraint programming via the black hole algorithm

Rodrigo Olivares, Ricardo Soto, Broderick Crawford, Marta Barria, Stefanie Niklander

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Resumen

In operation research and optimization area, Autonomous Search is a technique that provides the solver the auto-adaptive capability, during search process. This technique aims to improve performance in the exploration of search tree, updating the enumeration strategy online. This task is controlled by a choice function (CF) which decides, based on performance indicators given from the solver, how the strategy must be updated. The relevance of indicators is handled via back hole algorithm, inspired on natural phenomenon that occurs in outer space. If choice function exhibits a poor performance, the strategy is replacement and solver continue exploring the search tree under new enumeration strategy. In this paper, we present an evaluation of the impact and efficient using 16 different carefully constructed choice functions. We employ as test bed a set of well-known constrain satisfaction problems. Encouraging experimental results are obtained in order to show which using choice functions is highly efficient, if want to control the search process, online way.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 2016 42nd Latin American Computing Conference, CLEI 2016
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781509016334
DOI
EstadoPublicada - 25 ene. 2017
Evento42nd Latin American Computing Conference, CLEI 2016 - Valparaiso, Chile
Duración: 10 oct. 201614 oct. 2016

Serie de la publicación

NombreProceedings of the 2016 42nd Latin American Computing Conference, CLEI 2016

Conferencia

Conferencia42nd Latin American Computing Conference, CLEI 2016
País/TerritorioChile
CiudadValparaiso
Período10/10/1614/10/16

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