Hybridization of Cartesian Genetic Programming and Differential Evolution for Generating Classifiers based on Neural Networks
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- @InProceedings{Melo-Neto:2018:CEC,
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author = "Johnathan {Melo Neto} and Heder Bernardino and
Helio Barbosa",
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title = "Hybridization of Cartesian Genetic Programming and
Differential Evolution for Generating Classifiers based
on Neural Networks",
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booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2018",
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editor = "Marley Vellasco",
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address = "Rio de Janeiro, Brazil",
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month = "8-13 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming",
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DOI = "doi:10.1109/CEC.2018.8477906",
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abstract = "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 modelling 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",
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notes = "WCCI2018",
- }
Genetic Programming entries for
Johnathan Mayke Melo Neto
Heder Soares Bernardino
Helio J C Barbosa
Citations