NK Hybrid Genetic Algorithm for Clustering
<|reference_start|>NK Hybrid Genetic Algorithm for Clustering: 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.<|reference_end|>
@article{tinós2024nk,
    title={NK Hybrid Genetic Algorithm for Clustering},
    author={Renato Tin'os, Liang Zhao, Francisco Chicano, Darrell Whitley},
    journal={IEEE Trans. Evol. Comput. 22(5): 748-761 (2018)},
    year={2024},
    doi={10.1109/TEVC.2018.2828643},
    archivePrefix={arXiv},
    eprint={2402.03813},
    primaryClass={cs.NE cs.LG}
}