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

Elizabeth Montero, María Cristina Riff

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferencia

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 originalInglés
Título de la publicación alojadaFoundations of Intelligent Systems - 17th International Symposium, ISMIS 2008, Proceedings
Páginas262-267
Número de páginas6
DOI
EstadoPublicada - 9 jun 2008
Evento17th International Symposium on Methodologies for Intelligent Systems, ISMIS 2008 - Toronto, Canadá
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

Conferencia

Conferencia17th International Symposium on Methodologies for Intelligent Systems, ISMIS 2008
PaísCanadá
CiudadToronto
Período20/05/0823/05/08

Áreas temáticas de ASJC Scopus

  • Ciencia computacional teórica
  • Informática (todo)

Huella Profundice en los temas de investigación de 'Self-calibrating strategies for evolutionary approaches that solve constrained combinatorial problems'. En conjunto forman una huella única.

  • 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