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Hybridization of Cartesian Genetic Programming and Differential Evolution for Generating Classifiers Based on Neural Networks

Despite the significance of Artificial Neural Networks (ANNs) in practical situations and the several works available in the literature, to adjust its parameters remains as a current problem. Hence, the advent of methods to assist users during this modeling is relevant. Three hybrid techniques based on Cartesian Genetic Programming (CGP) and Differential Evolution (DE) are proposed here for the construction of ANNs. The developed methods carry out an uncoupled evolution of the topology (using CGP) and the weights (using DE). The ANNs are evolved for classification problems, and seven benchmark datasets are used in the computational experiments. Results show the superiority of the proposed methods when compared to other techniques from the literature.