Skip to main content

C-Strategy: A Dynamic Adaptive Strategy for the CLONALG Algorithm

  • Chapter
Transactions on Computational Science VIII

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 6260))

  • 403 Accesses

Abstract

The control of parameters during the execution of bio-inspired algorithms is an open research area. In this paper, we propose a new parameter control strategy for the immune algorithm CLONALG. Our approach is based on reinforcement learning ideas. We focus our attention on controlling the number of clones. Our approach provides an efficient and low cost adaptive technique for parameter control. We use instances of the Travelling Salesman Problem. The results obtained are very encouraging.

Partially Supported by the Fondecyt Project 1080110.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Birattari, M., Stützle, T., Paquete, L., Varrentrapp, K.: A racing algorithm for configuring metaheuristics. In: Proceedings of the Genetic and Evolutionary Computation Conference, New York-United States, July 2002, pp. 11–18. Morgan Kaufmann, San Francisco (2002)

    Google Scholar 

  2. De Castro, L.N., Von Zuben, F.: The clonal selection algorithm with engineering applications. In: Proceedings of Workshop on Artificial Immune Systems and their Apllications, GECCO, pp. 36–37. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  3. Davis, L.: Adapting operator probabilities in genetic algorithms. In: Proceedings of the third international conference on Genetic algorithms, pp. 61–69. Morgan Kaufmann, San Francisco (1989)

    Google Scholar 

  4. de Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  5. Deb, K., Agrawal, S.: Understanding interactions among genetic algorithm parameters. In: Foundations of Genetic Algorithms, vol. 5, pp. 265–286. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  6. Eiben, A.E., Hinterding, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation 3, 124–141 (1999)

    Article  Google Scholar 

  7. Eiben, A.E., Marchiori, E., Valkó, V.A.: 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)

    Google Scholar 

  8. Garrett, S.M.: Parameter-free, adaptive clonal selection. In: IEEE Congress on Evolutionary Computation, vol. 1, pp. 1052–1058 (2004)

    Google Scholar 

  9. Gómez, J.: Self adaptation of operador rates in evolutionary algorithms. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 1162–1173. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  10. Hinterding, R., Michalewicz, Z., Eiben, A.E.: Adaptation in evolutionary computation: A survey. In: IEEE International Conference on Evolutionary Computation, pp. 65–69 (1997)

    Google Scholar 

  11. Hu, J., Guo, C., Li, T., Yin, J.: Adaptive clonal selection with elitism-guided crossover for function optimization. In: International Conference on Innovative Computing, Information and Control, pp. 206–209 (2006)

    Google Scholar 

  12. Hutter, F., Hoos, H., Stützle, T.: Automatic algorithm configuration based on local search. In: Proceedings of the Twenty-Second Conference on Artifical Intelligence, pp. 1152–1157 (2007)

    Google Scholar 

  13. Lobo, F.G., Goldberg, D.E.: The parameter-less genetic algorithm in practice. Information Sciences 167(1-4), 217–232 (2004)

    Article  MATH  Google Scholar 

  14. Mezura-Montes, E., Palomeque-Ortiz, A.G.: Parameter control in differential evolution for constrained optimization. In: IEEE International Conference on E-Commerce Technology, pp. 1375–1382 (2009)

    Google Scholar 

  15. Montero, E., Riff, M.C., Basterrica, D.: Improving MMAS using parameter control. In: IEEE Congress on Evolutionary Computation, Hong-Kong, June 2008, pp. 4007–4011 (2008)

    Google Scholar 

  16. Montero, E., Riff, M.C.: Self-calibrating strategies for evolutionary approaches that solve constrained combinatorial problems. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds.) Foundations of Intelligent Systems. LNCS (LNAI), vol. 4994, pp. 262–267. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  17. Montiel, O., Castillo, O., Melin, P., Díaz, A.R., Sepúlveda, R.: Human evolutionary model: A new approach to optimization. Information Sciences 177(10), 2075–2098 (2007)

    Article  Google Scholar 

  18. Moscato, P., Fontanari, J.F.: Stochastic versus deterministic update in simulated annealing. Physics Letters A 146(4), 204–208 (1990)

    Article  Google Scholar 

  19. Nannen, V., Eiben, A.E.: Relevance estimation and value calibration of evolutionary algorithm parameters. In: Joint International Conference for Artificial Intelligence (IJCAI), pp. 975–980 (2006)

    Google Scholar 

  20. Pelikan, M., Goldberg, D.E., Lobo, F.G.: A Survey of Optimization by Building and Using Probabilistic Models. Computational Optimization and Applications 21(1), 5–20 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  21. Richter, D., Goldengorinand, B., Jäger, G., Molitor, P.: Improving the efficiency of helsgauns lin-kernighan heuristic for the symmetric tsp. In: Proceedings of the Fourth Workshop on Combinatorial and Algorithmic Aspects of Networking, pp. 99–111 (2007)

    Google Scholar 

  22. Riff, M.C., Bonnaire, X.: Inheriting parents operators: a new dynamic strategy to improve evolutionary algorithms. In: Hacid, M.-S., Raś, Z.W., Zighed, D.A., Kodratoff, Y. (eds.) ISMIS 2002. LNCS (LNAI), vol. 2366, pp. 333–341. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  23. Smith, J.E., Fogarty, T.C.: Operator and parameter adaptation in genetic algorithms. Soft Computing - A Fusion of Foundations, Methodologies and Applications 1(2), 81–87 (1997)

    Google Scholar 

  24. Srinivasa, K.G., Venugopal, K.R., Patnaik, L.M.: A self-adaptive migration model genetic algorithm for data mining applications. Information Sciences 177(20), 4295–4313 (2007)

    Article  MATH  Google Scholar 

  25. Stützle, T., Hoos, H.: Max-min ant system and local search for the traveling salesman problem. In: IEEE International Conference on Evolutionary Computation, pp. 309–314 (1997)

    Google Scholar 

  26. Stützle, T., Grün, A., Linke, S., Rüttger, M.: A comparison of nature inspired heuristics on the traveling salesman problem. In: Proceedings of the Parallel Problem Solving from Nature (PPSN VI), pp. 661–670. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  27. Sun, W.-D., Xu, X.-S., Dai, H.-W., Tang, Z., Tamura, H.: An immune optimization algorithm for tsp problem. In: SICE 2004 Annual Conference, vol. 1, pp. 710–715 (2004)

    Google Scholar 

  28. Tan, K.C., Chiam, S.C., Mamun, A.A., Goh, C.K.: Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization. European Journal of Operational Research 197(2), 701–713 (2009)

    Article  MATH  Google Scholar 

  29. Tuson, A., Ross, P.: Adapting operator settings in genetic algorithms. Evolutionary Computation 6(2), 161–184 (1998)

    Article  Google Scholar 

  30. Yang, J., Wu, C., Pueh Lee, H., Liang, Y.: Solving traveling salesman problems using generalized chromosome genetic algorithm. Progress in Natural Science 18(7), 887–892 (2008)

    Article  MathSciNet  Google Scholar 

  31. Zhang, W., Looks, M.: A novel local search algorithm for the traveling salesman problem that exploits backbones. In: Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI 2005), pp. 343–350 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Riff, M.C., Montero, E., Neveu, B. (2010). C-Strategy: A Dynamic Adaptive Strategy for the CLONALG Algorithm. In: Gavrilova, M.L., Tan, C.J.K. (eds) Transactions on Computational Science VIII. Lecture Notes in Computer Science, vol 6260. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16236-7_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-16236-7_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16235-0

  • Online ISBN: 978-3-642-16236-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics