AbstractClustering has been an area of intensive research for several decades because of its multifaceted applications in innumerable domains. Clustering can be either Boolean, where a single data point belongs to exactly one cluster, or fuzzy, where a single data point can have nonzero belongingness to more than one cluster. Traditionally, optimization of some well‐defined objective function has been the standard approach in both clustering and fuzzy clustering. Hence, researchers have investigated the utility of evolutionary computing and related techniques in this regard. The different approaches differ in their choice of the objective function and/or the optimization strategy used. In particular, clustering using genetic algorithms (GAs) has attracted attention of researchers, and has been studied extensively. This paper presents a short review of some of different approaches of GA‐based clustering methods. Two techniques, one with fixed number of clusters and another with a variable number of fuzzy clusters, are described along with some experimental results on numerical as well as image data sets. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 524–531 DOI: 10.1002/widm.47This article is categorized under: Algorithmic Development > Structure Discovery Technologies > Structure Discovery and Clustering