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Solving multiple instances at once: the role of search and adaptation

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Abstract

Having in mind the idea that the computational effort and knowledge gained while solving a problem’s instance should be used to solve other ones, we present a new strategy that allows to take advantage of both aspects. The strategy is based on a set of operators and a basic learning process that is fed up with the information obtained while solving several instances. The output of the learning process is an adjustment of the operators. The instances can be managed sequentially or simultaneously by the strategy, thus varying the information available for the learning process. The method has been tested on different SAT instance classes and the results confirm that (a) the usefulness of the learning process and (b) that embedding problem specific algorithms into our strategy, instances can be solved faster than applying these algorithms instance by instance.

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Notes

  1. We had also considered to equally distribute those \(E - E^\prime\) evaluations not used by instance \(j\) among the remaining ones. However, as the results of this scheme were worse than the ones obtained by the scheme presented here we decided to omit them in the paper.

  2. The reader should note that the computational time per evaluation or local search step is longer for gsat/tabu than for wsat and wsat/tabu. Nevertheless, we decide to use the number of local search steps as an efficiency measure since it has been used in very known works, as Hoos and Stützle (2000a) and Schuurmans and Southey (2001). A detailed study about the CPU time per step for these SAT solvers can be found in Hoos and Stützle (2000a).

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Acknowledgments

A.D. Masegosa is supported by the scholarship program FPI from the Spanish Ministry of Science and Innovation. This work has been partially funded by the project TIN2008-01948 from the Spanish Ministry of Science and Innovation and P07-TIC-02970 from the Andalusian Government. The authors wish to thank the anonymous reviewers for their useful comments.

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  1. Models of Decision and Optimization Research Group, Department of Computer Science and AI, University of Granada, 18071, Granada, Spain

    Antonio D. Masegosa, David A. Pelta & Juan R. González

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  1. Antonio D. Masegosa
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  2. David A. Pelta
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  3. Juan R. González
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Correspondence to Antonio D. Masegosa.

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Masegosa, A.D., Pelta, D.A. & González, J.R. Solving multiple instances at once: the role of search and adaptation. Soft Comput 15, 233–250 (2011). https://doi.org/10.1007/s00500-010-0564-4

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