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

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...

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Bibliographic Details
Published in:Journal of heuristics Vol. 7; no. 6; pp. 551 - 564
Main Authors: French, Alan P., Robinson, Andrew C., Wilson, John M.
Format: Journal Article
Language:English
Published: Boston Springer Nature B.V 01.11.2001
Subjects:
Algorithms
Genetic algorithms
Heuristic
Integer programming
Linear programming
Mathematical programming
Optimization
Software
Studies
Variables
ISSN:1381-1231, 1572-9397
Online Access:Get full text
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Summary: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. [PUBLICATION ABSTRACT]
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:1381-1231
1572-9397
DOI:10.1023/A:1011921025322