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Variable Dependency in Local Search: Prevention Is Better Than Cure

  • Steven Prestwich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4501)

Abstract

Local search achieves good results on a variety of SAT problems and often scales up better than backtrack search. But despite recent advances in local search heuristics it has failed to solve some structured problems, while backtrack search has advanced greatly on such problems. We conjecture that current modelling practices are unintentionally biased in favour of solution by backtrack search. To test this conjecture we remodel two problems whose large instances have long resisted solution by local search: parity learning and Towers of Hanoi as STRIPS planning. By reducing variable dependencies and using other techniques we boost local search performance by several orders of magnitude in both cases, and we can now solve 32-bit and 6-disk instances for the first time using a standard SAT local search algorithm.

Keywords

Local Search Parity Constraint Constraint Program Local Search Algorithm Cardinality Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Steven Prestwich
    • 1
  1. 1.Cork Constraint Computation Centre, Department of Computer Science, University College, CorkIreland

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