  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.
