Self-calibrating strategies for evolutionary approaches that solve constrained combinatorial problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 4994 LNAI, pages 262-267, 2008. doi abstract bibtex 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. © 2008 Springer-Verlag Berlin Heidelberg.
@inproceedings{10.1007/978-3-540-68123-6_29,
abstract = "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. © 2008 Springer-Verlag Berlin Heidelberg.",
year = "2008",
title = "Self-calibrating strategies for evolutionary approaches that solve constrained combinatorial problems",
volume = "4994 LNAI",
pages = "262-267",
doi = "10.1007/978-3-540-68123-6\_29",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"
}
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