RT Journal Article
T1 NK Hybrid Genetic Algorithm for Clustering
A1 Tinós, Renato
A1 Zhao, Liang
A1 Chicano-García, José-Francisco
A1 Whitley, L. Darrell
K1 Algoritmos genéticos
AB The NK hybrid genetic algorithm for clustering is proposed in this paper. In order to evaluate the solutions, the hybrid algorithm uses the NK clustering validation criterion 2 (NKCV2). NKCV2 uses information about the disposition of N small groups of objects. Each group is composed of K+1 objects of the dataset. Experimental results show that density-based regions can be identified by using NKCV2 with fixed small K. In NKCV2, the relationship between decision variables is known, which in turn allows us to apply gray box optimization. Mutation operators, a partition crossover, and a local search strategy are proposed, all using information about the relationship between decision variables. In partition crossover, the evaluation function is decomposed into q independent components; partition crossover then deterministically returns the best among 2^q possible offspring with computational complexity O(N). The NK hybrid genetic algorithm allows the detection of clusters with arbitrary shapes and the automatic estimation of the number of clusters. In the experiments, the NK hybrid genetic algorithm produced very good results when compared to another genetic algorithm approach and to state-of-art clustering algorithms.
YR 2018
FD 2018
LK https://hdl.handle.net/10630/29920
UL https://hdl.handle.net/10630/29920
LA eng
NO Renato Tinós, Liang Zhao, Francisco Chicano, L. Darrell Whitley: NK Hybrid Genetic Algorithm for Clustering. IEEE Trans. Evol. Comput. 22(5): 748-761 (2018)
NO In Brazil, this research was partially funded by FAPESP (2015/06462-1, 2015/50122-0, and 2013/07375-0) and CNPq (303012/2015-3 and 304400/2014-9). In Spain, this research was partially funded by Ministerio de Economía y Competitividad (TIN2014-57341-R and TIN2017-88213-R) and by Ministerio de Educación Cultura y Deporte (CAS12/00274).
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026