Self-calibrating strategies for evolutionary approaches that solve constrained combinatorial problems

Elizabeth Montero, María Cristina Riff

Resultado de la investigación: Conference contribution

6 Citas (Scopus)

Resumen

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.

Idioma originalEnglish
Título de la publicación alojadaFoundations of Intelligent Systems - 17th International Symposium, ISMIS 2008, Proceedings
Páginas262-267
Número de páginas6
DOI
EstadoPublished - 9 jun 2008
Evento17th International Symposium on Methodologies for Intelligent Systems, ISMIS 2008 - Toronto, Canada
Duración: 20 may 200823 may 2008

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen4994 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conference

Conference17th International Symposium on Methodologies for Intelligent Systems, ISMIS 2008
PaísCanada
CiudadToronto
Período20/05/0823/05/08

Huella dactilar

Constraint satisfaction problems
Combinatorial Problems
Evolutionary algorithms
Tuning
Constraint Satisfaction Problem
Parameter Tuning
Testing
Control Strategy
Evolutionary Algorithms
Evaluate
Strategy

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Citar esto

Montero, E., & Riff, M. C. (2008). Self-calibrating strategies for evolutionary approaches that solve constrained combinatorial problems. En Foundations of Intelligent Systems - 17th International Symposium, ISMIS 2008, Proceedings (pp. 262-267). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4994 LNAI). https://doi.org/10.1007/978-3-540-68123-6_29
Montero, Elizabeth ; Riff, María Cristina. / Self-calibrating strategies for evolutionary approaches that solve constrained combinatorial problems. Foundations of Intelligent Systems - 17th International Symposium, ISMIS 2008, Proceedings. 2008. pp. 262-267 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Montero, E & Riff, MC 2008, Self-calibrating strategies for evolutionary approaches that solve constrained combinatorial problems. En Foundations of Intelligent Systems - 17th International Symposium, ISMIS 2008, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4994 LNAI, pp. 262-267, 17th International Symposium on Methodologies for Intelligent Systems, ISMIS 2008, Toronto, Canada, 20/05/08. https://doi.org/10.1007/978-3-540-68123-6_29

Self-calibrating strategies for evolutionary approaches that solve constrained combinatorial problems. / Montero, Elizabeth; Riff, María Cristina.

Foundations of Intelligent Systems - 17th International Symposium, ISMIS 2008, Proceedings. 2008. p. 262-267 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4994 LNAI).

Resultado de la investigación: Conference contribution

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Montero E, Riff MC. Self-calibrating strategies for evolutionary approaches that solve constrained combinatorial problems. En Foundations of Intelligent Systems - 17th International Symposium, ISMIS 2008, Proceedings. 2008. p. 262-267. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-68123-6_29