A hybrid stochastic genetic–GRASP algorithm for clustering analysisA hybrid stochastic genetic–GRASP algorithm for clustering analysis Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-11-182008enThis paper presents a new stochastic methodology, which is based on the concepts of genetic algorithms (GAs) and greedy randomized adaptive search procedure (GRASP), for optimally clustering N objects into K clusters. The proposed stochastic algorithm (Hybrid GEN–GRASP) for the solution of the clustering problem is a two phase algorithm which combines a genetic algorithm for the solution of the feature selection problem and a GRASP algorithm for the solution of the clustering problem. Due to the nature of stochastic and population-based search, the proposed algorithm can overcome the drawbacks of traditional clustering methods. Its performance is compared with another methodology that uses for the solution of the feature selection problem a very popular metaheuristic method, the Tabu Search algorithm. Results from the application of the methodology to data sets from the UCI Machine Learning Repository are presented.http://creativecommons.org/licenses/by/4.0/Operational Research, An International Journal8133-46 Zopounidis Konstantinos Ζοπουνιδης Κωνσταντινος Michael Doumpos Δουμπος Μιχαλης Marinaki Magdalini Μαρινακη Μαγδαληνη Marinakis Ioannis Μαρινακης Ιωαννης Matsatsinis Nikolaos Ματσατσινης Νικολαος Springer Verlag Genetic algorithms GRASP Clustering analysis Feature selection problem