Paper Info
Title
A graph-based immune-inspired constraint satisfaction search
Abstract
We propose an artificial immune algorithm to solve constraint satisfaction problems (CSPs). Recently, bio-inspired algorithms have been proposed to solve CSPs. They have shown to be efficient in solving hard problem instances. Given that recent publications indicate that immune-inspired algorithms offer advantages to solve complex problems, our main goal is to propose an efficient immune algorithm which can solve CSPs. We have calibrated our algorithm using relevance estimation and value calibration (REVAC), which is a new technique recently introduced to find the parameter values for evolutionary algorithms. The tests were carried out using randomly generated binary constraint satisfaction problems and instances of the three-colouring problem with different constraint networks. The results suggest that the technique may be successfully applied to solve CSPs.
Year
DOI
Venue
2010
10.1007/s00521-010-0390-8
Neural Computing and Applications
Keywords
DocType
Volume
immune-inspired algorithms offer advantage,graph-based immune-inspired constraint satisfaction,binary constraint satisfaction problem,bio-inspired algorithm,constraint satisfaction problem,evolutionary algorithm,hard problem instance,efficient immune algorithm,artificial immune algorithm,different constraint network,complex problem
Journal
19
Issue
ISSN
Citations 
8
1433-3058
6
PageRank 
References 
Authors
0.60
11
3
Name
Order
Citations
PageRank
María Cristina Riff120023.91
Marcos Zúñiga2111.29
Elizabeth Montero36910.14