Multi-classifier systems, also known as ensembles or committees, have been widely used to solve several classification problems, because they usually provide better performance than the individual classifiers. However, in order to build robust ensembles, it is necessary that the individual classifiers are as accurate as diverse among themselves - this is known as the diversity/accuracy dilemma. In this sense, some works analyzing the ensemble performance in context of this dilemma have been proposed. However, the majority of them address the homogenous structures of ensemble, i.e., ensembles composed only of the same type of classifiers. Thus, motivated by this limitation, this paper will perform an empirical investigation on the diversity/accuracy dilemma for heterogeneous ensembles. In order to do this, genetic algorithms will be used to guide the building of the ensemble systems.