Some Insight into Genetic Algorithm as Clustering Technique

R. Baragona, L. Bocci, and C.M. Medaglia (Italy)


Genetic Algorithms, Cluster Analysis, Monte Carlo Simulation


In this article the performance of the genetic algorithms for solving some clustering problems is investigated through a simulation experiment. If the number of clusters is known in advance, our results show that the genetic algorithm is able to find the right partition, almost irrespective of the genetic parameters selected. Also, the genetic algorithm always performs favorably with respect to the K-means al gorithm. On the other hand, if the number of clusters is unknown, the genetic algorithm provides good results as well. Four versions of the genetic algorithm proposed in the literature are compared, and their performances are not found to differ significantly. Moreover, if the points are not equally partitioned into clusters, the performances de teriorate considerably. On the contrary, other perturbation sources, such as outliers or data errors, do not affect the results.

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