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Choice functions for Autonomous search in constraint Pro gramming: GA vs PSO

Soto, Ricardo and Crawford, Broderick and Misra, Sanjay and Palma, Wenceslao and Monfroy, Eric and Castro, Carlos and Paredes, Fernando (2013) Choice functions for Autonomous search in constraint Pro gramming: GA vs PSO. Tehnicki vjesnik / Technical Gazette, 20 (4). pp. 621-627.

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The variable and value ordering heuristics are a key element in Constraint Programming. Known together as the enumeration strategy they may have important consequences on the solving process. However, a suitable selection of heuristics is quite hard as their behaviour is complicated to predict. Autonomous search has been recently proposed to handle this concern. The idea is to dynamically replace strategies that exhibit poor performances by more promising ones during the solving process. This replacement is carried out by a choice function, which evaluates a given strategy in a given amount of time via quality indicators. An important phase of this process is performed by an optimizer, which aims at finely tuning the choice function in order to guarantee a precise evaluation of strategies. In this paper we evaluate the performance of two powerful choice functions: the first one supported by a genetic algorithm and the second one by a particle swarm optimizer. We present interesting results and we demonstrate the feasibility of using those optimization techniques for Autonomous Search in a Constraint Programming context

Item Type: Article
Uncontrolled Keywords: CONSTRAINT programming (Computer science) *FUNCTIONAL analysis *SEARCH algorithms *HEURISTIC algorithms *PROBLEM solving *PERFORMANCE evaluation *FEASIBILITY studies
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Mrs Patricia Nwokealisi
Date Deposited: 23 Mar 2014 20:12
Last Modified: 23 Mar 2014 20:12

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