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A number of different computational approaches have been applied in many different biology application domains. When such tools are based on conventional computation techniques, they have shown limitations to approach complex biological problems. In the present study, a genetic algorithm (named GANEL) that is based on some Never-Ending Learning (NEL) principles, is proposed as a tool to extract classification rules from biological datasets. The main goal of the proposed approach is to allow the discovery of concise, yet accurate, high-level rules (from a biological database) which can be used to describe the stronger patterns present in the biological data, revealing concise and relevant information about the application domain, as well as, be used as a classification system. More than focusing only on the classification accuracy, the proposed GANEL approach aims at balancing prediction precision, interpretability and comprehensibility. The obtained results show that the proposed GANEL is promising and capable of extracting useful high-level knowledge that could not be extracted by traditional classifications methods such as Decision Trees, One R and the Single Conjunctive Rule Learner, among others. Moreover, the accuracy of GANEL results (using a small set of attributes per class) are better than Computational Evolutionary Environment (CEE) (previously proposed in the literature) which was designed to the same problem domain.