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Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials

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Artificial Immune Systems (ICARIS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3627))

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Abstract

This paper presents a comparative study of two important Clonal Selection Algorithms (CSAs): CLONALG and opt-IA. To deeply understand the performance of both algorithms, we deal with four different classes of problems: toy problems (one-counting and trap functions), pattern recognition, numerical optimization problems and NP-complete problem (the 2D HP model for protein structure prediction problem). Two possible versions of CLONALG have been implemented and tested. The experimental results show a global better performance of opt-IA with respect to CLONALG. Considering the results obtained, we can claim that CSAs represent a new class of Evolutionary Algorithms for effectively performing searching, learning and optimization tasks.

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References

  1. Burnet, F.M.: The Clonal Selection Theory of Acquired Immunity. Cambridge University Press, Cambridge (1959)

    Google Scholar 

  2. Cutello, V., Nicosia, G.: The Clonal Selection Principle for in silico and in vitro Computing. In: de Castro, L.N., Von Zuben, F.J. (eds.) Recent Developments in Biologically Inspired Computing (2004)

    Google Scholar 

  3. De Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Paradigm. Springer, London (2002)

    Google Scholar 

  4. De Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Trans. on Evolutionary Computation 6(3), 239–251 (2002)

    Article  Google Scholar 

  5. Cutello, V., Nicosia, G., Pavone, M.: Exploring the capability of immune algorithms: A characterization of hypermutation operators. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 263–276. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  6. De Castro, L.N., Timmis, J.: An Artificial Immune Network for Multimodal Function Optimization. In: CEC 2002, Proceeding of IEEE Congress on Evolutionary Computation. IEEE Press, Los Alamitos (2002)

    Google Scholar 

  7. Prugel-Bennett, A., Rogers, A.: Modelling GA Dynamics. In: Proc. Theoretical Aspects of Evolutionary Computing, pp. 59–86 (2001)

    Google Scholar 

  8. Nijssen, S., Back, T.: An analysis of the Behavior of Simplified Evolutionary Algorithms on Trap Functions. IEEE Trans. on Evolutionary Computation 7(1), 11–22 (2003)

    Article  Google Scholar 

  9. Nicosia, G., Cutello, V., Pavone, M., Narzisi, G., Sorace, G.: How to Escape Traps using Clonal Selection Algorithms. In: The First International Conference on Informatics in Control, Automation and Robotics (ICINCO), vol. 1, pp. 322–326. INSTICC Press (2004)

    Google Scholar 

  10. Yao, X., Liu, Y., Lin, G.M.: Evolutionary programming made faster. IEEE Trans. on Evolutionary Computation 3, 82–102 (1999)

    Article  Google Scholar 

  11. Dill, K.A.: Theory for the folding and stability of globular proteins. Biochemistry 24, 1501–1509 (1985)

    Article  Google Scholar 

  12. Crescenzi, P., Goldman, D., Papadimitriou, C., Piccolboni, A., Yannakakis, M.: On the complexity of protein folding. J. of Comp. Bio. 5, 423–466 (1998)

    Article  Google Scholar 

  13. Krasnogor, N., Hart, W.E., Smith, J., Pelta, D.A.: Protein Structure Prediction with Evolutionary Algorithms. In: GECCO 1999, vol. 2, pp. 1596–1601 (1999)

    Google Scholar 

  14. Cutello, V., Morelli, G., Nicosia, G., Pavone, M.: Immune Algorithms with Aging operators for the String Folding Problem and the Protein Folding Problem. In: Raidl, G.R., Gottlieb, J. (eds.) EvoCOP 2005. LNCS, vol. 3448, pp. 80–90. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  15. Nicosia, G., Cutello, V., Pavone, M.: An Immune Algorithm with Hyper-Macromutations for the Dill’s 2D Hydrophobic-Hydrophilic Model. In: Congress on Evolutionary Computation, CEC 2004, vol. 1, pp. 1074–1080. IEEE Press, Los Alamitos (2004)

    Google Scholar 

  16. Goldberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms, vol. 7. Kluwer Academic Publisher, Boston (2002)

    MATH  Google Scholar 

  17. Cutello, V., Nicosia, G.: An Immunological Approach to Combinatorial Optimization Problems. In: Garijo, F.J., Riquelme, J.-C., Toro, M. (eds.) IBERAMIA 2002. LNCS (LNAI), vol. 2527. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  18. Nicosia, G., Cutello, V., Pavone, M.: A Hybrid Immune Algorithm with Information Gain for the Graph Coloring Problem. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 171–182. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  19. Garrett, S.M.: Parameter-free, Adaptive Clonal Selection. In: Congress on Evolutionary Computing, Portland Oregon (June 2004)

    Google Scholar 

  20. Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J.: Artificial Immune Systems. In: Third International Conference, ICARIS 2004, Catania, Italy, September 13-16, Springer, Heidelberg (2004)

    Google Scholar 

  21. Nicosia, G.: Immune Algorithms for Optimization and Protein Structure Prediction. PHD Thesis, University of Catania, Italy (December 2004)

    Google Scholar 

  22. Cutello, V., Narzisi, G., Nicosia, G.: A Class of Pareto Archived Evolution Strategy Algorithms Using Immune Inspired Operators for Ab-Initio Protein Structure Prediction. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 54–63. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  23. Garrett, S.M.: A Survey of Artificial Immune Systems: Are They Useful? Evolutionary Computation 13(2) (2005) (to appear)

    Google Scholar 

  24. Holland, J.: Genetic algorithms and the optimal allocation of trials. SIAM J. Computing 2, 88–105 (1973)

    Article  MATH  MathSciNet  Google Scholar 

  25. Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  26. Rechenberg, I.: Evolutions strategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution. Frommann-Holzboog, Stuttgart (1973)

    Google Scholar 

  27. Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence Through Simulated Evolution. Wiley Publishing, New York (1966)

    MATH  Google Scholar 

  28. Koza, J.R.: Evolving a computer program to generate random numbers using the genetic programming paradigm. In: Proc. of the Fourth Int. Conf. on GA (1991)

    Google Scholar 

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Cutello, V., Narzisi, G., Nicosia, G., Pavone, M. (2005). Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds) Artificial Immune Systems. ICARIS 2005. Lecture Notes in Computer Science, vol 3627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536444_2

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  • DOI: https://doi.org/10.1007/11536444_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28175-7

  • Online ISBN: 978-3-540-31875-0

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