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Inc*: An Incremental Approach for Improving Local Search Heuristics

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Book cover Evolutionary Computation in Combinatorial Optimization (EvoCOP 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4972))

Abstract

This paper presents Inc*, a general algorithm that can be used in conjunction with any local search heuristic and that has the potential to substantially improve the overall performance of the heuristic. Genetic programming is used to discover new strategies for the Inc* algorithm. We experimentally compare performance of local heuristics for SAT with and without the Inc* algorithm. Results show that Inc* consistently improves performance.

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Authors and Affiliations

  1. Department of Computing and Electronic Systems, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, United Kingdom

    Mohamed Bader-El-Den & Riccardo Poli

Authors
  1. Mohamed Bader-El-Den
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  2. Riccardo Poli
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Editor information

Jano van Hemert Carlos Cotta

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Bader-El-Den, M., Poli, R. (2008). Inc*: An Incremental Approach for Improving Local Search Heuristics. In: van Hemert, J., Cotta, C. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2008. Lecture Notes in Computer Science, vol 4972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78604-7_17

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  • DOI: https://doi.org/10.1007/978-3-540-78604-7_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78603-0

  • Online ISBN: 978-3-540-78604-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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