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Regular Grammatical Inference: A Genetic Algorithm Approach

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Advances in Soft Computing — AFSS 2002 (AFSS 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2275))

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Abstract

Grammatical inference is the problem of inferring a grammar, given a set of positive samples which the inferred grammar should accept and a set of negative samples which the grammar should not accept. Here we apply genetic algorithm for inferring regular languages. The genetic search is started from maximal canonical automaton built from structurally complete sample. In view of limiting the increasing complexity as the sample size grows, we have edited structurally complete sample. We have tested our algorithm for 16 languages and have compared our results with previous works of regular grammatical inference using genetic algorithm. The results obtained confirm the feasibility of applying genetic algorithm for regular grammatical inference.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology, 400 076, Bombay, Mumbai, India

    Pravin Pawar & G. Nagaraja

Authors
  1. Pravin Pawar
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  2. G. Nagaraja
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Editor information

Editors and Affiliations

  1. Electronics and Communication Sciences Unit, Indian Statistical Institute, 203 B.T. Road, 700108, Calcutta, India

    Nikhil R. Pal

  2. Brain Science Institute, RIKEN, 2-1 Hirosawa, Wako, Japan

    Michio Sugeno

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© 2002 Springer-Verlag Berlin Heidelberg

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Pawar, P., Nagaraja, G. (2002). Regular Grammatical Inference: A Genetic Algorithm Approach. In: Pal, N.R., Sugeno, M. (eds) Advances in Soft Computing — AFSS 2002. AFSS 2002. Lecture Notes in Computer Science(), vol 2275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45631-7_58

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  • DOI: https://doi.org/10.1007/3-540-45631-7_58

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43150-3

  • Online ISBN: 978-3-540-45631-5

  • eBook Packages: Springer Book Archive

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