Institutional Repository
Technical University of Crete
Technical University of Crete
EN
|
EL
| URI: | http://purl.tuc.gr/dl/dias/167DFC2F-94BC-46B0-B4E6-800FF41DD71B | ||
| Year | 2008 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
|
||
| Bibliographic Citation | Y. Marinakis, M. Marinaki, M. Doumpos, N. Matsatsinis and C. Zopounidis, "A hybrid stochastic genetic–GRASP algorithm for clustering analysis," Operation. Res., vol. 8, no. 1, pp. 33-46, May 2008. doi:10.1007/s12351-008-0004-8 https://doi.org/10.1007/s12351-008-0004-8 | ||
| Appears in Collections |
This 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.
Copyright © DIAS 2013
