Ensemble classifiers are very useful tools and can be applied in many real world applications for classifying unseen data patterns into one of the known or unknown classes. However, there are many problems facing ensemble classifiers such as finding appropriate number of layers, clusters or even base classifiers which can produce best diversity and accuracy. There has been very little research conducted in this area and there is lack of an automatic method to find these parameters. This paper presents an evolutionary algorithm based approach to identify the optimal number of layers and clusters in hierarchical neural ensemble classifiers. The proposed approach has been evaluated on UCI machine learning benchmark datasets. A comparative analysis of results using the proposed approach and recently published approaches in the literature is presented in this paper.