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QG/GA: a stochastic search for Progol

Stephen Muggleton, Alireza Tamaddoni-Nezhad
2007 Machine Learning  
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.  ...  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.  ...  Section 3 describes an implementation of QG and QG/GA in Progol.  ... 
doi:10.1007/s10994-007-5029-3 fatcat:3nvtzldb55aolguydzmbsmt5zq

QG/GA: A Stochastic Search for Progol [chapter]

Stephen Muggleton, Alireza Tamaddoni-Nezhad
Lecture Notes in Computer Science  
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.  ...  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.  ...  Section 3 describes an implementation of QG and QG/GA in Progol.  ... 
doi:10.1007/978-3-540-73847-3_9 fatcat:yy3ocawar5b5rby5bcxigyxs7y

Guest editorial: special issue on Inductive Logic Programming

Stephen Muggleton, Ramon Otero, Simon Colton
2007 Machine Learning  
Two of the papers are related to search in ILP. Firstly, a novel stochastic search technique is investigated in the paper, "QG/GA: A Stochastic Search for Progol" (Muggleton and Tamaddoni-Nezhad).  ...  This was a theory prize-winner, and describes a new algorithm for efficiently constructing randomly-chosen consistent hypotheses within the Progol framework.  ...  Also in this general area, the paper "Generalized Ordering-search for Learning Directed Probabilistic Logical Models" (Ramon, Croonenborghs, Fierens, Blockeel and Bruynooghe) introduces an algorithm to  ... 
doi:10.1007/s10994-007-5036-4 fatcat:m5kfm7qwwneb3ibfnnbl73qlam

Logic-based machine learning using a bounded hypothesis space: the lattice structure, refinement operators and a genetic algorithm approach

Alireza Tamaddoni Nezhad, Stephen H. Muggleton
2016
We also discuss genetic search for learning first-order clauses and describe a framework for genetic and stochastic refinement search for bounded subsumption. on.  ...  In this thesis we also show how refinement operators can be adapted for a stochastic search and give an analysis of refinement operators within the framework of stochastic refinement search.  ...  In this chapter we also described Quick Generalisation (QG) algorithm and QG/GA search which are implemented in GA-Progol.  ... 
doi:10.25560/29849 fatcat:kgzoveed45fv7dneirx7ef5dqu

Efficient Learning and Evaluation of Complex Concepts in Inductive Logic Programming

Jose Carlos Almeida Santos Santos, Stephen Muggleton, Michael Sternberg, Wellcome Trust
2011
In ILP, logic programming, a subset of first-order logic, is used as a uniform representation language for the problem specification and induced theories.  ...  These learning problems are interesting both from a research perspective, as they raise the standards for ILP systems, and from an application perspective, where these target concepts naturally occur in  ...  However, when the background knowledge is non-determinate and a linear search is used, as in QG/GA, computing the coverage of a clause costs O(c.n.m).  ... 
doi:10.25560/6409 fatcat:yom47vguibeu3gtqszlspoel7i

OASIcs, Volume 35, ICCSW'13, Complete Volume [article]

Andrew V. Jones, Nicholas Ng
2013
This research has been supported by a grant of the Romanian National Authority for Scientific Research, CNCS-UEFISCDI, project number PN-II-ID-PCE-2011-3-0439.  ...  This work has also been supported by: the EraSysBio+ grant SHIPREC from the European Union, BBSRC and BMBF; a VLA grant on machine learning algorithms; a grant from the National Natural Science Foundation  ...  Muggleton and A. Tamaddoni-Nezhad. QG/GA: a stochastic search for Progol. Mach. Learn., 70:121-133, 2008. 17 S.H. Nienhuys-Cheng and R. de Wolf.  ... 
doi:10.4230/oasics.iccsw.2013 fatcat:nzu3mwmc3bg47pgg6lmeygo7uu