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Advances in Nature and Biologically Inspired Computing pp 151–163Cite as

A Study of Genetic Programming and Grammatical Evolution for Automatic Object-Oriented Programming: A Focus on the List Data Structure

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 419))

Abstract

Automatic programming is a concept which until today has not been fully achieved using evolutionary algorithms. Despite much research in this field, a lot of the concepts remain unexplored. The current study is part of ongoing research aimed at using evolutionary algorithms for automatic programming. The performance of two evolutionary algorithms, namely, genetic programming and grammatical evolution are compared for automatic object-oriented programming. Genetic programming is an evolutionary algorithm which searches a program space for a solution program. A program generated by genetic programming is executed to yield a solution to the problem at hand. Grammatical evolution is a variation of genetic programming which adopts a genotype–phenotype distinction and uses grammars to map from a genotypic space to a phenotypic (program) space. The study implements and tests the abilities of these approaches as well as a further variation of genetic programming, namely, object-oriented genetic programming, for automatic object-oriented programming. The application domain used to evaluate these approaches is the generation of abstract data types, specifically the class for the list data structure. The study also compares the performance of the algorithms when human programmer problem domain knowledge is incorporated and when such knowledge is not incorporated. The results show that grammatical evolution performs better than genetic programming and object-oriented genetic programming, with object-oriented genetic programming outperforming genetic programming. Future work will focus on evolution of programs that use the evolved classes.

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Author information

Authors and Affiliations

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

    Kevin Igwe & Nelishia Pillay

Authors
  1. Kevin Igwe
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  2. Nelishia Pillay
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Corresponding author

Correspondence to Kevin Igwe .

Editor information

Editors and Affiliations

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

    Nelishia Pillay

  2. Sch of IT, Dept of CS, University of Pretoria, Pretoria, South Africa

    Andries P. Engelbrecht

  3. Sci N/w for Innova and Research Exc, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  4. Department of Computing Sciences,, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa

    Mathys C. du Plessis

  5. Faculty of Electrical Engineering and, Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Václav Snášel

  6. Fakulti Teknologi Maklumat dan Komunikas, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Azah Kamilah Muda

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Igwe, K., Pillay, N. (2016). A Study of Genetic Programming and Grammatical Evolution for Automatic Object-Oriented Programming: A Focus on the List Data Structure. In: Pillay, N., Engelbrecht, A., Abraham, A., du Plessis, M., Snášel, V., Muda, A. (eds) Advances in Nature and Biologically Inspired Computing. Advances in Intelligent Systems and Computing, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-319-27400-3_14

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  • DOI: https://doi.org/10.1007/978-3-319-27400-3_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27399-0

  • Online ISBN: 978-3-319-27400-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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