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.

翻译：图划分问题（GPP）是优化领域中最具挑战性的模型之一。由于其NP难性质，研究者将兴趣转向遗传算法（GA）等近似方法。基于边的遗传算法在求解GPP时已展现出可观的结果。然而，对于大规模稠密实例，编码表示规模过大，影响了遗传算法的效率。本文通过缩减GPP边集，研究了修改染色体规模对基于边的遗传算法性能的影响。我们考察了不同缩减程度下遗传算法的表现，并报告了所获结果。