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Abstract: The recent years have witnessed a growing interest in two advanced strategies to cope with the data clustering problem, namely, clustering ensembles and multi-objective clustering. In this paper, we present a genetic programming based approach that can be considered as a hybrid of these strategies, thereby allowing that different hierarchical clustering ensembles be simultaneously evolved taking into account complementary validity indices. Results of computational experiments conducted with artificial and real datasets indicate that, in most of the cases, at least one of the Pareto optimal partitions returned by the proposed approach compares favorably or go in par with the consensual partitions yielded by two well-known clustering ensemble methods in terms of clustering quality, as gauged by the corrected Rand index. [ABSTRACT FROM AUTHOR]