A new grouping genetic algorithm for clustering problems

作者：Agustin Blas L E; Salcedo Sanz S^*; Jimenez Fernandez S; Carro Calvo L; Del Ser J; Portilla Figueras J A

来源：Expert Systems with Applications, 2012, 39(10): 9695-9703.

DOI：10.1016/j.eswa.2012.02.149

• Grouping genetic algorithms
• Clustering problems
• Hybrid algorithms

In this paper we present a novel grouping genetic algorithm for clustering problems. Though there have been different approaches that have analyzed the performance of several genetic and evolutionary algorithms in clustering, the grouping-based approach has not been, to our knowledge, tested in this problem yet. In this paper we fully describe the grouping genetic algorithm for clustering, starting with the proposed encoding, different modifications of crossover and mutation operators, and also the description of a local search and an island model included in the algorithm, to improve the algorithm%26apos;s performance in the problem. We test the proposed grouping genetic algorithm in several experiments in synthetic and real data from public repositories, and compare its results with that of classical clustering approaches, such as K-means and DBSCAN algorithms, obtaining excellent results that confirm the goodness of the proposed grouping-based methodology.

• 出版日期2012-8