This paper presents an approach that deals with the feature selection problem, and includes two main aspects: first, the selection is done during the evolutionary learning process, i.e., it is a dynamic approach; and second, the selection is local, i.e., the algorithm selects the best features from the best space region to learn at a given time of the exploration process. While the traditional feature selection is based on the attribute relevance, our approach is based on a new concept, called feature influence, which is aware of the dynamics and locality of the concept. The feature influence provides a measure of the attribute relevance at a certain instant of the evolutionary learning process, since it depends on each generation. Experimental results have been obtained by comparing an EA--based supervised learning algorithm to its modified version to include the concept approached. The results show an excellent performance, as the new adapted algorithm achieves the same classification results while using less rules, less conditions in rules and much less generations. The experiments include the statistical significance of the improvement over a set of sixteen datasets from the UCI repository.
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