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

  • Riccardo Poli
Evolutionary Machine Learning and Classifier Systems
Part of the Lecture Notes in Computer Science book series (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.

Keywords

Genetic Programming Active Node Crossover Point Parse Tree Finite State Automaton 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Riccardo Poli
    • 1
  1. 1.School of Computer ScienceThe University of BirminghamBirminghamUK

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