The Importance of Topology Evolution in NeuroEvolution: A Case Study Using Cartesian Genetic Programming of Artificial Neural Networks
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- @InProceedings{Turner2013a,
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author = "Andrew James Turner and Julian Francis Miller",
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title = "The Importance of Topology Evolution in
NeuroEvolution: A Case Study Using Cartesian Genetic
Programming of Artificial Neural Networks",
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booktitle = "Research and Development in Intelligent Systems XXX",
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year = "2013",
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editor = "Max Bramer and Miltos Petridis",
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pages = "213--226",
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address = "Cambridge",
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month = "10-12 " # dec,
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organisation = "British Computer Society's Specialist Group on
Artificial Intelligence",
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publisher = "Springer International Publishing",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-319-02620-6",
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URL = "http://dx.doi.org/10.1007/978-3-319-02621-3_15",
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DOI = "doi:10.1007/978-3-319-02621-3_15",
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abstract = "NeuroEvolution (NE) is the application of evolutionary
algorithms to Artificial Neural Networks (ANN). This
paper reports on an investigation into the relative
importance of weight evolution and topology evolution
when training ANN using NE. This investigation used the
NE technique Cartesian Genetic Programming of
Artificial Neural Networks (CGPANN). The results
presented show that the choice of topology has a
dramatic impact on the effectiveness of NE when only
evolving weights; an issue not faced when manipulating
both weights and topology. This paper also presents the
surprising result that topology evolution alone is far
more effective when training ANN than weight evolution
alone. This is a significant result as many methods
which train ANN manipulate only weights.",
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notes = "http://www.bcs-sgai.org/ai2013/ Incorporating
Applications and Innovations in Intelligent Systems XXI
Proceedings of AI-2013, The Thirty-third SGAI
International Conference on Innovative Techniques and
Applications of Artificial Intelligence",
- }
Genetic Programming entries for
Andrew James Turner
Julian F Miller
Citations