Evolving multi-dimensional wavelet neural networks for classification using Cartesian Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{Khan:2017:Neurocomputing,
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author = "Maryam Mahsal Khan and Alexandre Mendes and
Ping Zhang and Stephan K. Chalup",
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title = "Evolving multi-dimensional wavelet neural networks for
classification using Cartesian Genetic Programming",
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journal = "Neurocomputing",
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volume = "247",
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pages = "39--58",
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year = "2017",
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ISSN = "0925-2312",
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DOI = "doi:10.1016/j.neucom.2017.03.048",
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URL = "http://www.sciencedirect.com/science/article/pii/S0925231217305635",
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abstract = "Wavelet Neural Networks (WNNs) are complex artificial
neural systems and their training can be a challenge.
In the past, most common training schemes for WNNs,
such as gradient descent, have been restricted to
training only a subset of differentiable parameters. In
this paper, we propose an evolutionary method to train
both differentiable and non-differentiable parameters
using the concept of Cartesian Genetic Programming
(CGP). The approach was evaluated on the two-spiral
task and on real-world datasets for the detection of
breast cancer and Parkinson's disease. In our
experiments, the performance of the proposed method was
comparable to several standard methods of
classification. On the breast cancer dataset, the
performance was better than other non-ensemble and
multistep processing methods. The experimental results
show how the performance of WNNs depends on the number
of wavelons used. The presented case studies
demonstrate that the proposed WNNs perform
competitively in comparison to several other methods
and results reported in literature.",
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keywords = "genetic algorithms, genetic programming,
Neuroevolution, Wavelet Neural Networks,
Classification",
- }
Genetic Programming entries for
Maryam Mahsal Khan
Alexandre Mendes
Ping Zhang
Stephan K Chalup
Citations