NeuroEvolution: Evolving Heterogeneous Artificial Neural Networks
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @Article{Turner2014f,
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author = "Andrew James Turner and Julian Francis Miller",
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title = "NeuroEvolution: Evolving Heterogeneous Artificial
Neural Networks",
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journal = "Evolutionary Intelligence",
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year = "2014",
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volume = "7",
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number = "3",
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pages = "135--154",
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month = nov,
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note = "Special Issue: Evolution in UK 20",
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keywords = "genetic algorithms, genetic programming, Cartesian
genetic programming, ANN, Heterogeneous Artificial
Neural Networks, NeuroEvolution, Evolutionary
Algorithms, Artificial Neural Networks, Computational
intelligence",
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publisher = "Springer",
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ISSN = "1864-5909",
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URL = "http://dx.doi.org/10.1007/s12065-014-0115-5",
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DOI = "doi:10.1007/s12065-014-0115-5",
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size = "20 pages",
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abstract = "NeuroEvolution is the application of Evolutionary
Algorithms to the training of Artificial Neural
Networks. Currently the vast majority of
NeuroEvolutionary methods create homogeneous networks
of user defined transfer functions. This is despite
NeuroEvolution being capable of creating heterogeneous
networks where each neuron's transfer function is not
chosen by the user, but selected or optimised during
evolution. This paper demonstrates how NeuroEvolution
can be used to select or optimise each neuron's
transfer function and empirically shows that doing so
significantly aids training. This result is important
as the majority of NeuroEvolutionary methods are
capable of creating heterogeneous networks using the
methods described.",
- }
Genetic Programming entries for
Andrew James Turner
Julian F Miller
Citations