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Is Genetic Programming Dependent on High-level Primitives?

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Artificial Neural Nets and Genetic Algorithms

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Abstract

The aim of this paper is to refute the claim that the success of genetic programming depends on problem-specific high-level primitives. We therefore apply genetic programming to the λ-calculus, a Turing complete formalism with only two (very low-level) primitives.

Genetic programming is suited to find the predecessor function in the space of λ-definable functions without a priori knowledge. The predecessor function is historically important and documented to be ‘a challenge’ and ‘difficult’ to find.

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Authors and Affiliations

  1. Institute for Information Systems, Vienna University of Technology, Auhofstraße 160/11, A-1130, Vienna, Austria

    D. Heiss-Czedik

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  1. D. Heiss-Czedik
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© 1998 Springer-Verlag Wien

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Heiss-Czedik, D. (1998). Is Genetic Programming Dependent on High-level Primitives?. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_89

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  • DOI: https://doi.org/10.1007/978-3-7091-6492-1_89

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-83087-1

  • Online ISBN: 978-3-7091-6492-1

  • eBook Packages: Springer Book Archive

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