While Genetic Algorithm (GA) is a powerful tool for combinatorial optimization, the vast population of candidate solutions it typically deploys and algorithm's intrinsic randomness lead to difficulty in understanding its search behavior. We discuss in this paper a clustering-based visualization tool for GA that attempts to mediate this problem. GA population across its entire generations are clustered, and each cluster and its individuals are mapped to a visual symbol. The tool enables a GA researcher or user to understand better the behavior of a GA run, specifically the local searches it performs in its global exploration to go from one generation to another. Copyright © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.