Using a Hybrid Genetic-Algorithm/Branch and Bound Approach to Solve Feasibility and Optimization Integer Programming Problems

  • Authors:
  • Alan P. French;Andrew C. Robinson;John M. Wilson

  • Affiliations:
  • Loughborough University, Loughborough, LE11 3TU, England, UK. a.p.french@lboro.ac.uk;Loughborough University, Loughborough, LE11 3TU, England, UK. a.c.robinson@lboro.ac.uk;Loughborough University, Loughborough, LE11 3TU, England, UK. j.m.wilson@lboro.ac.uk

  • Venue:
  • Journal of Heuristics
  • Year:
  • 2001

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Abstract

The satisfiability problem in forms such as maximum satisfiability (MAX-SAT) remains a hard problem. The most successful approaches for solving such problems use a form of systematic tree search. This paper describes the use of a hybrid algorithm, combining genetic algorithms and integer programming branch and bound approaches, to solve MAX-SAT problems. Such problems are formulated as integer programs and solved by a hybrid algorithm implemented within standard mathematical programming software. Computational testing of the algorithm, which mixes heuristic and exact approaches, is described.