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QG/GA: A Stochastic Search for Progol

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Inductive Logic Programming (ILP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4455))

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Abstract

A search approach is presented, based on a novel algorithm called QG (Quick Generalisation). QG carries out a random-restart stochastic bottom-up search which efficiently generates a consistent clause on the fringe of the refinement graph search without needing to explore the graph in detail. We use a Genetic Algorithm (GA) to evolve and re-combine clauses generated by QG. Initial experiments with QG/GA indicate that this approach can be more efficient than standard refinement-graph searches, while generating similar or better solutions.

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References

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Authors and Affiliations

  1. Department of Computing, Imperial College, London,  

    Stephen Muggleton & Alireza Tamaddoni-Nezhad

Authors
  1. Stephen Muggleton
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  2. Alireza Tamaddoni-Nezhad
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Stephen Muggleton Ramon Otero Alireza Tamaddoni-Nezhad

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© 2007 Springer-Verlag Berlin Heidelberg

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Muggleton, S., Tamaddoni-Nezhad, A. (2007). QG/GA: A Stochastic Search for Progol. In: Muggleton, S., Otero, R., Tamaddoni-Nezhad, A. (eds) Inductive Logic Programming. ILP 2006. Lecture Notes in Computer Science(), vol 4455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73847-3_9

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  • DOI: https://doi.org/10.1007/978-3-540-73847-3_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73846-6

  • Online ISBN: 978-3-540-73847-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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