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International Conference on Neural Networks and Artificial Intelligence

ICNNAI 2014: Neural Networks and Artificial Intelligence pp 159–170Cite as

A Hybrid Genetic Algorithm and Radial Basis Function NEAT

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 440))

Abstract

We propose a new neuroevolution technique that makes use of genetic algorithm to improve the task provided to a Radial Basis Function – NEAT algorithm. Normally, Radial Basis Function works best when the input- output mapping is smooth, that is, the dimensionality is high. However, if the input changes abruptly, for example, for fractured problems, efficient mapping cannot happen. Thus, the algorithm cannot solve such problems effectively. We make use of genetic algorithm to emulate the smoothing parameter in the Radial Basis function. In the proposed algorithm, the input- output mapping is done in a more efficient manner due to the ability of genetic algorithm to approximate almost any function. The technique has been successfully applied in the non- Markovian double pole balancing without velocity and the car racing strategy. It is shown that the proposed technique significantly outperforms classical neuroevolution techniques in both of the above benchmark problems.

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, University of Mauritius, Le Reduit, Moka, Mauritius

    Heman Mohabeer & K. M. Sunjiv Soyjaudah

Authors
  1. Heman Mohabeer
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  2. K. M. Sunjiv Soyjaudah
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Editor information

Editors and Affiliations

  1. Brest State Technical University, Moskowskaja 267, 224017, Brest, Belarus

    Vladimir Golovko  & Akira Imada  & 

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Mohabeer, H., Soyjaudah, K.M.S. (2014). A Hybrid Genetic Algorithm and Radial Basis Function NEAT. In: Golovko, V., Imada, A. (eds) Neural Networks and Artificial Intelligence. ICNNAI 2014. Communications in Computer and Information Science, vol 440. Springer, Cham. https://doi.org/10.1007/978-3-319-08201-1_15

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  • DOI: https://doi.org/10.1007/978-3-319-08201-1_15

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08200-4

  • Online ISBN: 978-3-319-08201-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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