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An Adaptive Metaheuristic for the Simultaneous Resolution of a Set of Instances

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Part of the Studies in Computational Intelligence book series (SCI,volume 129)

Abstract

Most of the adaptive metaheuristics face the resolution of an instance from scratch, without considering previous runs. Basing on the idea that the computa- tional effort done and knowledge gained when solving an instance should be use to solve similar ones, we present a new metaheuristic strategy that permits the simul- taneous solution of a group of instances. The strategy is based on a set of adaptive operators that works on several sets of solutions belonging to different problem in- stances. The method has been tested on MAX-SAT with sets of various instances obtaining promising results.

Keywords

  • Memetic Algorithm
  • Conjunctive Normal Form
  • Truth Assignment
  • Sequential Search
  • Boolean Formula

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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References

  1. J. Crawford and L. Auton. Experimental results on the crossover pint in random 3sat. Artificial Intelligence, 81(1–2):31–57, 1996.

    CrossRef  MathSciNet  Google Scholar 

  2. M. Garey and D. Johnson. Computers and Intractability: a Guide to the Theory of NP-completeness. W. H. Freeman, 1979.

    Google Scholar 

  3. W. Hart, N. Krasnogor, and J. Smith, editors. Recent Advances in Memetic Algorithms. Studies in Fuzziness and Soft Computing. Physica-Verlag, 2004.

    Google Scholar 

  4. H. Hoos and T. Stutzle. Local search algorithms for sat: An empirical evaluation. Journal of Automated Reasoning, 24:421–481, 2000.

    MATH  CrossRef  Google Scholar 

  5. H. Hoos and T. Stutzle. Satlib: An online resource for research on sat. In H. van Maaren I. P. Gent and T. Walsh, editors, SAT2000, pages 283–292. IOS Press, 2000.

    Google Scholar 

  6. J. Mateo and L. de la Ossa. Lio: Tool for metaheuristics. http://www.info-ab.uclm.es/simd/SOFTWARE/LIO/, 2006.

  7. Y.-S. Ong, M.-H. Lim, N. Zhu, and K.-W. Wong. Classification of adaptive memetic algorithms:a comparative study. IEEE transactions on systems, man, and cyberneticsart b: cybernetics, 36(1), 2006.

    Google Scholar 

  8. D. Pelta, A. Blanco, and J. Verdegay. A fuzzy valuation-based local search framework for combinatorial optimization problems. Journal of Fuzzy Optimization and Decision Making, 1(2):177–193, 2002.

    MATH  CrossRef  MathSciNet  Google Scholar 

  9. D. Pelta and N. Krasnogor. Multimeme algorithms using fuzzy logic based memes for protein structure prediction. In W. Hart, N. Krasnogor, and J. Smith, editors, Recent Advances in Memetic Algorithms, Studies in Fuzziness and Soft Computing. Physica-Verlag, 2004.

    Google Scholar 

  10. B. Selman, H. Kautz, and B. Cohen. Noise strategies for improving local search. In Proceedings of the Twelfth National Conference on Artificial Intelligence, pages 337 – 343. MIT Press, 1994.

    Google Scholar 

  11. J. Smith. Coevolving memetic algorithms: A review and progress report. IEEE Transactions On Systems, Man, And Cyberneticsart B: Cybernetics, 37(1), 2007.

    Google Scholar 

  12. J. Smith. Credit assignment in adaptive memetic algorithms. In Proceedings of GECCO 2007, 2007.

    Google Scholar 

  13. M. Yokoo. Why adding more constraints makes a problem easier for hill-climbing algorithms: Analyzing landscapes of csps. Lecture Notes In Computer Sciences, 1330:357–370, 1997.

    Google Scholar 

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Masegosa, A.D., Royo, A.S., Pelta, D. (2008). An Adaptive Metaheuristic for the Simultaneous Resolution of a Set of Instances. In: Krasnogor, N., Nicosia, G., Pavone, M., Pelta, D. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2007). Studies in Computational Intelligence, vol 129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78987-1_12

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  • DOI: https://doi.org/10.1007/978-3-540-78987-1_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78986-4

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