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Adaptive Evolutionary Algorithms and Extensions to the HyFlex Hyper-heuristic Framework

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7492)

Abstract

HyFlex is a recently proposed software framework for implementing hyper-heuristics and domain-independent heuristic optimisation algorithms [13]. Although it was originally designed to implement hyper-heuristics, it provides a population and a set of move operators of different types. This enable the implementation of adaptive versions of other heuristics such as evolutionary algorithms and iterated local search. The contributions of this article are twofold. First, a number of extensions to the HyFlex framework are proposed and implemented that enable the design of more effective adaptive heuristics. Second, it is demonstrated that adaptive evolutionary algorithms can be implemented within the framework, and that the use of crossover and a diversity metric produced improved results, including a new best-known solution, on the studied vehicle routing problem.

Keywords

  • Search Operator
  • Iterate Local Search
  • Borda Count
  • Guide Local Search
  • Adaptive Large Neighborhood Search

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. The Cross-domain Heuristic Search Challenge (CHeSC 2011). Website (2011), http://www.asap.cs.nott.ac.uk/external/chesc2011/

  2. Battiti, R., Brunato, M., Mascia, F.: Reactive Search and Intelligent Optimization. Operations research/Computer Science Interfaces, vol. 45. Springer (2008)

    Google Scholar 

  3. Bleuler, S., Laumanns, M., Thiele, L., Zitzler, E.: PISA – A Platform and Programming Language Independent Interface for Search Algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 494–508. Springer, Heidelberg (2003)

    CrossRef  Google Scholar 

  4. Burke, E.K., Curtois, T., Hyde, M., Kendall, G., Ochoa, G., Petrovic, S., Vazquez-Rodriguez, J.A., Gendreau, M.: Iterated local search vs. hyper-heuristics: Towards general-purpose search algorithms. In: IEEE Congress on Evolutionary Computation, CEC 2010, Barcelona, Spain, pp. 3073–3080 (July 2010)

    Google Scholar 

  5. Burke, E.K., Gendreau, M., Ochoa, G., Walker, J.D.: Adaptive iterated local search for cross-domain optimisation. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 1987–1994. ACM, New York (2011)

    CrossRef  Google Scholar 

  6. Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Woodward, J.: A Classification of Hyper-heuristic Approaches. In: Handbook of Metaheuristics, vol. 146, ch. 15, pp. 449–468. Springer (2010)

    Google Scholar 

  7. Chan, C.Y., Xue, F., Ip, W.H., Cheung, C.F.: A hyper-heuristic inspired by pearl hunting. In: International Conference on Learning and Intelligent Optimization (LION 6). LNCS, Springer (to appear, 2012)

    Google Scholar 

  8. Cowling, P.I., Kendall, G., Soubeiga, E.: A Hyperheuristic Approach to Scheduling a Sales Summit. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, pp. 176–190. Springer, Heidelberg (2001)

    CrossRef  Google Scholar 

  9. Fialho, Á., Da Costa, L., Schoenauer, M., Sebag, M.: Extreme Value Based Adaptive Operator Selection. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN X. LNCS, vol. 5199, pp. 175–184. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

  10. Kubiak, M.: Distance measures and fitness-distance analysis for the capacitated vehicle routing problem. In: Metaheuristics. Operations Research/Computer Science Interfaces Series, vol. 39, pp. 345–364. Springer US (2007)

    Google Scholar 

  11. Mascia, F., Stutzle, T.: A non-adaptive stochastic local search algorithm for the chesc 2011 competition. In: Proceedings of Learning and Intelligent Optimization 6th International Conference, LION 6, Paris, France, January 16-20. LNCS, Springer (to appear, 2012)

    Google Scholar 

  12. Maturana, J., Saubion, F.: A Compass to Guide Genetic Algorithms. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN X. LNCS, vol. 5199, pp. 256–265. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

  13. Ochoa, G., Hyde, M., Curtois, T., Vazquez-Rodriguez, J.A., Walker, J., Gendreau, M., Kendall, G., McCollum, B., Parkes, A.J., Petrovic, S., Burke, E.K.: HyFlex: A Benchmark Framework for Cross-Domain Heuristic Search. In: Hao, J.-K., Middendorf, M. (eds.) EvoCOP 2012. LNCS, vol. 7245, pp. 136–147. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  14. Ong, Y.S., Lim, M.H., Zhu, N., Wong, K.W.: Classification of adaptive memetic algorithms: a comparative study. IEEE Transactions on Systems, Man, and Cybernetics, Part B 36(1), 141–152 (2006)

    CrossRef  Google Scholar 

  15. SINTEF. VRPTW benchmark problems, on the SINTEF transport optimisation portal. Website (2011), http://www.sintef.no/Projectweb/TOP/Problems/VRPTW/

  16. Voudouris, C., Tsang, E.: Guided local search and its application to the traveling salesman problem. European Journal of Operational Research 113(2), 469–499 (1999)

    MATH  CrossRef  Google Scholar 

  17. Walker, J.D., Ochoa, G., Gendreau, M., Burke, E.K.: Vehicle routing and adaptive iterated local search within the hyflex hyper-heuristic framework. In: International Conference on Learning and Intelligent Optimization (LION 6). LNCS. Springer (to appear, 2012)

    Google Scholar 

  18. Wauters, T., Vancroonenburg, W., Vanden-Berghe, G.: A guide-and-observe hyper-heuristic approach to the eternity ii puzzle. Journal of Mathematical Modelling and Algorithms, 1–17 (2012), doi:10.1007/s10852-012-9178-4

    Google Scholar 

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Ochoa, G., Walker, J., Hyde, M., Curtois, T. (2012). Adaptive Evolutionary Algorithms and Extensions to the HyFlex Hyper-heuristic Framework. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32964-7_42

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  • DOI: https://doi.org/10.1007/978-3-642-32964-7_42

  • Publisher Name: Springer, Berlin, Heidelberg

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