Paper Info
Title
Autonomous Search in Constraint Satisfaction via Black Hole: A Performance Evaluation Using Different Choice Functions.
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.
Year
DOI
Venue
2016
10.1007/978-3-319-41000-5_6
ADVANCES IN SWARM INTELLIGENCE, ICSI 2016, PT I
Keywords
Field
DocType
Autonomous Search,Constraint programming,Constraint satisfaction,Optimization,Choice functions
Constraint satisfaction,Mathematical optimization,Computer science,Constraint programming,Black hole,Solver,Optimization problem,Hybrid algorithm (constraint satisfaction),Choice function
Conference
Volume
ISSN
Citations 
9712
0302-9743
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Ricardo Soto119447.59
Broderick Crawford244673.74
Rodrigo Olivares3459.07
Stefanie Niklander422.38
Eduardo Olguín5309.86