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abstract

Evolving instances of unconstrained binary quadratic programming that challenge a tabu search heuristic

Published: 07 July 2012 Publication History

Abstract

A Tabu Search heuristic for unconstrained binary quadratic programming performs perfectly on a range of random problem instances. A genetic algorithm searches spaces of UBQP instances for instances that challenge the heuristic. The GA's evaluation step compares the performance of the Tabu Search to that of a memetic algorithm on the candidate instance being evaluated. On UBQP instances evolved by the GA, the TS heuristic returns solutions that are inferior to those of the memetic algorithm by significant margins.

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Published In

cover image ACM Conferences
GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
July 2012
1586 pages
ISBN:9781450311786
DOI:10.1145/2330784

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2012

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Author Tags

  1. difficult instances
  2. genetic algorithm
  3. tabu search
  4. unconstrained binary quadratic programming

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GECCO '12
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GECCO '12: Genetic and Evolutionary Computation Conference
July 7 - 11, 2012
Pennsylvania, Philadelphia, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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