Fast learning neural networks using Cartesian genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Khan:2013:Neurocomputing,
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author = "Maryam Mahsal Khan and Arbab Masood Ahmad and
Gul Muhammad Khan and Julian F. Miller",
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title = "Fast learning neural networks using Cartesian genetic
programming",
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journal = "Neurocomputing",
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year = "2013",
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volume = "121",
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pages = "274--289",
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month = "9 " # dec,
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keywords = "genetic algorithms, genetic programming, cartesian
genetic programming, Artificial neural network, Pole
balancing, Breast cancer, Neuroevolution, Recurrent
networks",
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ISSN = "0925-2312",
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DOI = "doi:10.1016/j.neucom.2013.04.005",
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URL = "http://www.sciencedirect.com/science/article/pii/S0925231213004499",
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URL = "http://results.ref.ac.uk/Submissions/Output/3354639",
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abstract = "A fast learning neuroevolutionary algorithm for both
feedforward and recurrent networks is proposed. The
method is inspired by the well known and highly
effective Cartesian genetic programming (CGP)
technique. The proposed method is called the CGP-based
Artificial Neural Network (CGPANN). The basic idea is
to replace each computational node in CGP with an
artificial neuron, thus producing an artificial neural
network. The capabilities of CGPANN are tested in two
diverse problem domains. Firstly, it has been tested on
a standard benchmark control problem: single and double
pole for both Markovian and non-Markovian cases.
Results demonstrate that the method can generate
effective neural architectures in substantially fewer
evaluations in comparison to previously published
neuroevolutionary techniques. In addition, the evolved
networks show improved generalisation and robustness in
comparison with other techniques. Secondly, we have
explored the capabilities of CGPANNs for the diagnosis
of Breast Cancer from the FNA (Finite Needle
Aspiration) data samples. The results demonstrate that
the proposed algorithm gives 99.5percent accurate
results, thus making it an excellent choice for pattern
recognitions in medical diagnosis, owing to its
properties of fast learning and accuracy. The power of
a CGP based ANN is its representation which leads to an
efficient evolutionary search of suitable topologies.
This opens new avenues for applying the proposed
technique to other linear/non-linear and
Markovian/non-Markovian control and pattern recognition
problems.",
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uk_research_excellence_2014 = "This work advances the evolution of
artificial neural networks. In collaboration with
University of Engineering Technology in Peshawar,
Pakistan, the work is the first journal publication on
the evolution of artificial neural networks using
Cartesian Genetic programming. Development of this work
by a PhD student won best student paper at
International Conference on Artificial Intelligence,
Cambridge, 2013.",
- }
Genetic Programming entries for
Maryam Mahsal Khan
Arbab Masood Ahmad
Gul Muhammad Khan
Julian F Miller
Citations