Edge-set reduction to efficiently solve the graph partitioning problem with the genetic algorithm
Abstract
The graph partitioning problem (GPP) is among the most challenging models in optimization. Because of its NP-hardness, the researchers directed their interest towards approximate methods such as the genetic algorithms (GA). The edge-based GA has shown promising results when solving GPP. However, for big dense instances, the size of the encoding representation becomes too huge and affects GA's efficiency. In this paper, we investigate the impact of modifying the size of the chromosomes on the edge based GA by reducing the GPP edge set. We study the GA performance with different levels of reductions, and we report the obtained results.
Cite
@article{arxiv.2307.10410,
title = {Edge-set reduction to efficiently solve the graph partitioning problem with the genetic algorithm},
author = {Ali Chaouche and Menouar Boulif},
journal= {arXiv preprint arXiv:2307.10410},
year = {2023}
}
Comments
This is a slightly modified exended version of a paper (in French) accepted in COSI'17 as a poster. The title of the originel paper is "Ali Chaouche and Menouar Boulif. Codage binaire avec seuil pour la resolution du probleme de partitionnement de graphes avec des algorithmes genetiques"