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Automated Design of Genetic Programming Classification Algorithms Using a Genetic Algorithm

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Book cover Applications of Evolutionary Computation (EvoApplications 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10200))

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Abstract

There is a large scale initiative by the machine learning community to automate the design of machine learning techniques to remove reliance on the human expert, providing out of the box software that can be used by novices. In this study the automated design of genetic programming classification algorithms is proposed. A number of design decisions have to be considered by algorithm designers during the design process and this is usually a time consuming task. Our automated design approach uses a genetic algorithm to automatically configure a genetic programming classification algorithm. The genetic algorithm determines parameter values and sets the flow control for the classification algorithm. The proposed system is tested on real world problems and the results indicate that induced classifiers perform better than manually designed classifiers.

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Authors and Affiliations

  1. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa

    Thambo Nyathi & Nelishia Pillay

Authors
  1. Thambo Nyathi
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  2. Nelishia Pillay
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Correspondence to Thambo Nyathi or Nelishia Pillay .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

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Nyathi, T., Pillay, N. (2017). Automated Design of Genetic Programming Classification Algorithms Using a Genetic Algorithm. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10200. Springer, Cham. https://doi.org/10.1007/978-3-319-55792-2_15

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  • DOI: https://doi.org/10.1007/978-3-319-55792-2_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55791-5

  • Online ISBN: 978-3-319-55792-2

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