QG/GA: a stochastic search for Progol |
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Authors: | Stephen Muggleton Alireza Tamaddoni-Nezhad |
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Affiliation: | (1) Department of Computing, Imperial College London, London, UK |
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Abstract: | Most search techniques within ILP require the evaluation of a large number of inconsistent clauses. However, acceptable clauses
typically need to be consistent, and are only found at the “fringe” of the search space. A search approach is presented, based
on a novel algorithm called QG (Quick Generalization). 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. In this QG/GA setting, QG is used to seed
a population of clauses processed by the GA. Experiments with QG/GA indicate that this approach can be more efficient than
standard refinement-graph searches, while generating similar or better solutions.
Editors: Ramon Otero, Simon Colton. |
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Keywords: | Stochastic search Refinement Genetic Algorithms |
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