Paper Info
Title
NAIS: a calibrated immune inspired algorithm to solve binary constraint satisfaction problems
Abstract
We propose in this paper an artificial immune system to solve CSPs. The algorithm has been designed following the framework proposed by de Castro and Timmis. We have calibrated our algorithm using Relevance Estimation and Value Calibration (REVAC), that is a new technique, recently introduced to find the parameter values for evolutionary algorithms. The tests were carried out using random generated binary constraint satisfaction problems on the transition phase where are the hardest problems. The algorithm shown to be able to find quickly good quality solutions.
Year
DOI
Venue
2007
10.1007/978-3-540-73922-7_3
ICARIS
Keywords
Field
DocType
parameter value,de castro,value calibration,relevance estimation,binary constraint satisfaction problem,hardest problem,good quality solution,artificial immune system,evolutionary algorithm,immune inspired algorithm,new technique,constraint satisfaction problem
Min-conflicts algorithm,Computer science,Artificial intelligence,Backtracking,Local consistency,Mathematical optimization,Algorithm,AC-3 algorithm,Constraint satisfaction problem,Machine learning,Difference-map algorithm,Binary constraint,Hybrid algorithm (constraint satisfaction)
Conference
Volume
ISSN
ISBN
4628
0302-9743
3-540-73921-1
Citations 
PageRank 
References 
0
0.34
13
Authors
3
Name
Order
Citations
PageRank
Marcos Zuñiga100.34
María Cristina Riff220023.91
Elizabeth Montero36910.14