Abstract—Gene Clustering is one of the most popular application in the field of Bioinformatics. It is a method of grouping gene into clusters, such that each cluster must have similar gene expression levels. The two most popular population based globalized search algorithms Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used for clustering gene expression data but the main drawback of these two algorithm’s is that they got trapped in local optima while exploring across the problem space. So the PSO and GA has been hybridized with several other methods, to overcome the local optima problem. This paper presents a literature survey of both GA and PSO’s application in Gene Clustering. PSO and GA variants and their hybridization with other metaheuristics are also described in this paper. An attempt is made to provide a guide for the researchers who are working in the area of population based gene clustering.