An evolutionary algorithm with acceleration operator to generate a subset of typical testors

This paper is focused on introducing a Hill-Climbing algorithm as a way to solve the problem of generating typical testors - or non-reducible descriptors - from a training matrix. All the algorithms reported in the state-of-the-art have exponential complexity. However, there are problems for which there is no need to generate the whole set of typical testors, but it suffices to find only a subset of them. For this reason, we introduce a Hill-Climbing algorithm that incorporates an acceleration operation at the mutation step, providing a more efficient exploration of the search space. The experiments have shown that, under the same circumstances, the proposed algorithm performs better than other related algorithms reported so far.