Automatically Generated Algorithms for the Vertex Coloring Problem
Cargando...
Archivos
Fecha
2013
Autores
Profesor/a Guía
Facultad/escuela
Idioma
en
Título de la revista
ISSN de la revista
Título del volumen
Editor
Public Library of Science
Nombre de Curso
Licencia CC
Attribution 4.0 International (CC BY 4.0)
Licencia CC
https://journals.plos.org/plosone/s/licenses-and-copyright
Resumen
The vertex coloring problem is a classical problem in combinatorial optimization that consists of assigning a color to each vertex of a graph such that no adjacent vertices share the same color, minimizing the number of colors used. Despite the various practical applications that exist for this problem, its NP-hardness still represents a computational challenge. Some of the best computational results obtained for this problem are consequences of hybridizing the various known heuristics. Automatically revising the space constituted by combining these techniques to find the most adequate combination has received less attention. In this paper, we propose exploring the heuristics space for the vertex coloring problem using evolutionary algorithms. We automatically generate three new algorithms by combining elementary heuristics. To evaluate the new algorithms, a computational experiment was performed that allowed comparing them numerically with existing heuristics. The obtained algorithms present an average 29.97% relative error, while four other heuristics selected from the literature present a 59.73% error, considering 29 of the more difficult instances in the DIMACS benchmark.
Notas
Indexación: Scopus.
Palabras clave
Metaheuristics, Chromatic Number, Combinatorial Optimization Problem, automation, calibration, classification algorithm, data analysis software, evolutionary algorithm, mathematical analysis
Citación
PLoS ONE, Volume 8, Issue 313, March 2013, Article number e58551
DOI
10.1371/journal.pone.0058551