QG/GA: a stochastic search for Progol

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. Keywords Stochastic search

• Type: Journal
• Year: 2008
• Where: ML
• Authors: Stephen Muggleton, Alireza Tamaddoni-Nezhad
• Keywords: Genetic Algorithms, Inconsistent Clauses, Machine Learning, ML 2008, Random-restart Stochastic Bottom-up