DepthLimited crossover in GP for classifier evolution

Genetic Programming (GP) provides a novel way of classification with key features like transparency, flexibility and versatility. Presence of these properties makes GP a powerful tool for classifier evolution. However, GP suffers from code bloat, which is highly undesirable in case of classifier evolution. In this paper, we have proposed an operator named ''DepthLimited crossover''. The proposed crossover does not let trees increase in complexity while maintaining diversity and efficient search during evolution. We have compared performance of traditional GP with DepthLimited crossover GP, on data classification problems and found that DepthLimited crossover technique provides compatible results without expanding the search space beyond initial limits. The proposed technique is found efficient in terms of classification accuracy, reduced complexity of population and simplicity of evolved classifiers.