An evolutionary algorithm with acceleration operator to generate a subset of typical testors
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Data
2014-05-01Autor
Sánchez-Díaz, Guillermo
Díaz-Sánchez, Germán
Mora-González, Miguel
Aguirre-Salado, Carlos A.
Huerta-Cuéllar, Guillermo
Piza-Dávila, Hugo I.
Reyes-Cárdenas, Óscar
Cárdenas-Tristán, Abraham
Metadata
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http://www.sciencedirect.com/science/article/pii/S0167865513004297https://hdl.handle.net/20.500.12032/69768
Descrição
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.ITESO, A.C.