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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Veröffentlicht in:Journal of heuristics Jg. 7; H. 6; S. 551 - 564
Hauptverfasser: French, Alan P., Robinson, Andrew C., Wilson, John M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer Nature B.V 01.11.2001
Schlagworte:
Algorithms
Genetic algorithms
Heuristic
Integer programming
Linear programming
Mathematical programming
Optimization
Software
Studies
Variables
ISSN:1381-1231, 1572-9397
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Zusammenfassung: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]
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:1381-1231
1572-9397
DOI:10.1023/A:1011921025322