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AISB International Workshop on Evolutionary Computing

AISB EC 1997: Evolutionary Computing pp 163–177Cite as

Parallel distributed genetic programming applied to the evolution of natural language recognisers

  • Evolutionary Machine Learning and Classifier Systems
  • Conference paper
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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1305))

Abstract

This paper describes an application of Parallel Distributed Genetic Programming (PDGP) to the problem of inducing recognisers for natural language from positive and negative examples. PDGP is a new form of Genetic Programming (GP) which is suitable for the development of programs with a high degree of parallelism and an efficient and effective reuse of partial results. Programs are represented in PDGP as graphs with nodes representing functions and terminals, and links representing the flow of control and results. PDGP allows the exploration of a large space of possible programs including standard tree-like programs, logic networks, neural networks, finite state automata, Recursive Transition Networks (RTNs), etc. The paper describes the representations, the operators and the interpreters used in PDGP, and describes how these can be tailored to evolve RTN-based recognisers.

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Authors and Affiliations

  1. School of Computer Science, The University of Birmingham, B15 2TT, Birmingham, UK

    Riccardo Poli

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  1. Riccardo Poli
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David Corne Jonathan L. Shapiro

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© 1997 Springer-Verlag Berlin Heidelberg

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Poli, R. (1997). Parallel distributed genetic programming applied to the evolution of natural language recognisers. In: Corne, D., Shapiro, J.L. (eds) Evolutionary Computing. AISB EC 1997. Lecture Notes in Computer Science, vol 1305. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0027173

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  • DOI: https://doi.org/10.1007/BFb0027173

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63476-8

  • Online ISBN: 978-3-540-69578-3

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