TY - GEN
T1 - Self-calibrating strategies for evolutionary approaches that solve constrained combinatorial problems
AU - Montero, Elizabeth
AU - Riff, María Cristina
N1 - Funding Information:
The authors were supported by the Fondecyt Project 1080110.
PY - 2008/6/9
Y1 - 2008/6/9
N2 - In this paper, we evaluate parameter control strategies for evolutionary approaches to solve constrained combinatorial problems. For testing, we have used two well known evolutionary algorithms that solve the Constraint Satisfaction Problems GSA and SAW. We contrast our results with REVAC, a recently proposed technique for parameter tuning.
AB - In this paper, we evaluate parameter control strategies for evolutionary approaches to solve constrained combinatorial problems. For testing, we have used two well known evolutionary algorithms that solve the Constraint Satisfaction Problems GSA and SAW. We contrast our results with REVAC, a recently proposed technique for parameter tuning.
KW - Evolutionary algorithms
KW - Parameter control
UR - http://www.scopus.com/inward/record.url?scp=44649144598&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-68123-6_29
DO - 10.1007/978-3-540-68123-6_29
M3 - Conference contribution
AN - SCOPUS:44649144598
SN - 3540681221
SN - 9783540681229
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 262
EP - 267
BT - Foundations of Intelligent Systems - 17th International Symposium, ISMIS 2008, Proceedings
T2 - 17th International Symposium on Methodologies for Intelligent Systems, ISMIS 2008
Y2 - 20 May 2008 through 23 May 2008
ER -