The knowledge acquired during the learning of artificial neural networks (ANNs) is coded as values in synaptic weights, which makes their interpretations difficult, hence the name of the black box. The aim of this work is to provide a comprehensible interpretation of the ANN's decisions by extracting symbolic rules. We improve the performance of our extraction algorithm by combining the ANN with a genetic algorithm. Misleading rules whose support and confidence values are less than fixed thresholds are removed and, as a result, the comprehensibility is improved. The extracted rules are evaluated and compared with other works. The results show good performance of our proposal in terms of fidelity and accuracy.