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In this paper, a twofold approach to improve the performance of genetic algorithms (GAs) in the feature selection problem (FSP) is presented. First, a novel genetic operator is introduced to solve the FSP. This operator fixes in each iteration the number of features to be selected among the available ones and consequently reduces the size of the search space. This approach yields two main advantages: a) training the learning machine becomes faster and b) a higher performance is achieved by using the selected subset. Second, we propose using the Walsh expansion of the FSP fitness function in order to perform ranking on the problem features. Ranking features have been traditionally considered to be a challenging problem, especially significant in health sciences where the number of available and potentially noisy signals is high. Three real biological datasets are used to test the behavior of the two approaches proposed.