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A Compass to Guide Genetic Algorithms

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Parallel Problem Solving from Nature – PPSN X (PPSN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5199))

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Abstract

Parameter control is a key issue to enhance performances of Genetic Algorithms (GA). Although many studies exist on this problem, it is rarely addressed in a general way. Consequently, in practice, parameters are often adjusted manually. Some generic approaches have been experimented by looking at the recent improvements provided by the operators. In this paper, we extend this approach by including operators’ effect over population diversity and computation time. Our controller, named Compass, provides an abstraction of GA’s parameters that allows the user to directly adjust the balance between exploration and exploitation of the search space. The approach is then experimented on the resolution of a classic combinatorial problem (SAT).

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References

  1. Eiben, A.E., Michalewicz, Z., Schoenauer, M., Smith, J.: Parameter Control in Evolutionary Algorithms. In: [20], pp. 19–46

    Google Scholar 

  2. Jong, K.D.: Parameter Setting in EAs: a 30 Year Perspective. In: [20], pp. 1–48

    Google Scholar 

  3. Meyer-Nieberg, S., Beyer, H.: Self-Adaptation in EAs. In: [20], pp. 47–75

    Google Scholar 

  4. Kee, E., Airey, S., Cyre, W.: An adaptive genetic algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 391–397. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  5. Maturana, J., Saubion, F.: Towards a generic control strategy for EAs: an adaptive fuzzy-learning approach. In: Proceedings of IEEE International Conference on Evolutionary Computation (CEC), pp. 4546–4553 (2007)

    Google Scholar 

  6. Wong, L., Leung, H.: A novel approach in parameter adaptation and diversity maintenance for GAs. Soft Computing 7(8), 506–515 (2003)

    Article  Google Scholar 

  7. Thierens, D.: Adaptive Strategies for Operator Allocation. In: [20], pp. 77–90

    Google Scholar 

  8. Igel, C., Kreutz, M.: Operator adaptation in structure optimization of neural networks. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), p. 1094. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  9. Lobo, F., Goldberg, D.: Decision making in a hybrid genetic algorithm. In: Proc. of IEEE Intl. Conference on Evolutionary Computation (CEC), pp. 122–125 (1997)

    Google Scholar 

  10. Whitacre, J., Pham, T., Sarker, R.: Use of statistical outlier detection method in adaptive evolutionary algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 1345–1352. ACM Press, New York (2006)

    Google Scholar 

  11. Eiben, A., Marchiori, E., Valkó, V.: Evolutionary algorithms with on-the-fly population size adjustment. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 41–50. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  12. Ursem, R.: Diversity-guided evolutionary algorithms. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 462–474. Springer, Heidelberg (2002)

    Google Scholar 

  13. Eiben, A., Horvath, M., Kowalczyk, W., Schut, M.: Reinforcement learning for online control of evolutionary algorithms. In: Brueckner, S.A., Hassas, S., Jelasity, M., Yamins, D. (eds.) ESOA 2006. LNCS (LNAI), vol. 4335, pp. 151–160. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  14. Lis, J.: Parallel genetic algorithm with dynamic control parameter. In: Proc. of IEEE Intl. Conference on Evolutionary Computation (CEC), pp. 324–329 (1996)

    Google Scholar 

  15. Tsutsui, S., Fujimoto, Y., Ghosh, A.: Forking GAs: GAs with search space division schemes. Evolutionary Computation 5(1), 61–80 (1997)

    Article  Google Scholar 

  16. Harik, G., Lobo, F.: A parameter-less GA. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 258–265 (1999)

    Google Scholar 

  17. Cook, S.A.: The complexity of theorem-proving procedures. In: STOC 1971: Proceedings of the third annual ACM symposium on Theory of computing, pp. 151–158. ACM Press, New York (1971)

    Chapter  Google Scholar 

  18. Hoos, H., Stützle, T.: SATLIB: An Online Resource for Research on SAT, pp. 283–292. IOS Press, Amsterdam (2000), www.satlib.org

    Google Scholar 

  19. Lardeux, F., Saubion, F., Hao, J.K.: GASAT: A genetic local search algorithm for the satisfiability problem. Evolutionary Computation 14(2), 223–253 (2006)

    Article  Google Scholar 

  20. Lobo, F., Lima, C., Michalewicz, Z. (eds.): Parameter Setting in Evolutionary Algorithms. Studies in Computational Intelligence, vol. 54. Springer, Heidelberg (2007)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. LERIA, Université d’Angers, 2, Bd Lavoisier, 49045, Angers, France

    Jorge Maturana & Frédéric Saubion

Authors
  1. Jorge Maturana
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  2. Frédéric Saubion
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Editor information

Editors and Affiliations

  1. Fakultät für Informatik, Technische Universität Dortmund, 44221, Dortmund, Germany

    Günter Rudolph

  2. Fakultät für Informatik, Technische Universität Dortmund, 44221, Dortmund, Germany

    Thomas Jansen  & Nicola Beume  & 

  3. Department of Computing and Electronic Systems, University of Essex, CO4 3SQ, Colchester, Essex, UK

    Simon Lucas

  4. Dipartimento di Ingegneria Meccanica, Università degli Studi di Trieste, 34127, Trieste, Italy

    Carlo Poloni

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© 2008 Springer-Verlag Berlin Heidelberg

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Maturana, J., Saubion, F. (2008). A Compass to Guide Genetic Algorithms. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds) Parallel Problem Solving from Nature – PPSN X. PPSN 2008. Lecture Notes in Computer Science, vol 5199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87700-4_26

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  • DOI: https://doi.org/10.1007/978-3-540-87700-4_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87699-1

  • Online ISBN: 978-3-540-87700-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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