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Sensitivity Analysis of an Autonomous Evolutionary Algorithm

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Advances in Artificial Intelligence – IBERAMIA 2012 (IBERAMIA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7637))

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Abstract

When applying any heuristic, the user faces difficulties on deciding on the control parameters of the method. A generic sensitivity analysis to measure the interdependencies of the parameters from an autonomous evolutionary algorithm and their influence in the final result is shown. The Multi Dynamics Algorithm for Global Optimization is the base of the experiment. With only two parameters, it is a quasi-free parameter autonomous algorithm. The impact on the quality of the results on several multimodal standard problems applying different instances of those parameters has been studied. Excellent outcomes for sensitivity levels from 100 to 10− 5 are found. The Logit model is used to determine the functioning parameters of the MAGO and for their mutual effects. Depending on the problem type, its dimensionality, and the expected precision, this work gives a priori configuring for the best performance of the MAGO.

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References

  1. Grefenstette, J.J.: Optimization of Control Parameters for Genetic Algorithms. IEEE Transactions on Systems, Man, and Cybernetics 16(1), 122–128 (1986)

    Article  Google Scholar 

  2. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computation. Springer (2003)

    Google Scholar 

  3. Lobo, F.G., Lima, C.F., Michalewicz, Z. (eds.): Parameter Setting in Evolutionary Algorithms. Springer (2007)

    Google Scholar 

  4. Hernández, J.A., Ospina, J.D.: A Multi Dynamics Algorithm for Global Optimization. Mathematical and Computer Modelling 52(7), 1271–1278 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  5. Czepiel, S.A.: Maximum Likelihood Estimation of Logistic Regression Models Theory and Implementation. czep.net, http://czep.net/stat/mlelr.pdf (accessed January 17, 2012)

  6. Jihong, Y., Muammer, K., Jay, L.: A Prognostic Algorithm for Machine Performance Assessment and its Application. Production Planning & Control 15(8), 796–801 (2004)

    Article  Google Scholar 

  7. Hosmer, D.W., Lemeshow, S.: Applied Logistic Regression. Wiley, New York (1989)

    MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  9. Hansen, N.: The CMA Evolution Strategy A Comparing Review. Studies in Fuziness and Soft Computing 192, 75–102 (2006)

    Article  Google Scholar 

  10. Eiben, A.E., Hinterding, R., Michalewicz, Z.: Parameter Control in Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation 3(2), 124–141 (1999)

    Article  Google Scholar 

  11. Šprogar, M.: Autonomous Evolutionary Algorithm. In: Fuerstner, I. (ed.) Products and Services from R & D to Final Solutions. Sciyo (2010)

    Google Scholar 

  12. Hamadi, Y., Monfroy, E., Saubion, F.: What is Autonomous Search? Springer Optimization and its Applications 45, 357–391 (2010)

    Article  MathSciNet  Google Scholar 

  13. Bäck, T.: Introduction to the Special Issue ( Self-Adaptation. Evolutionary Computation 9(2), 3–4 (2001)

    Article  Google Scholar 

  14. Yu, X., Gen, M.: Introduction to Evolutionary Algorithms. Springer, London (2010)

    Book  MATH  Google Scholar 

  15. Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization. Tech. Report 2005005. Kanpur Genetic Algorithms Lab. (2005)

    Google Scholar 

  16. Montgomery, D.C.: Introduction to Statistical Quality Control. Wiley (2008)

    Google Scholar 

  17. Hedar, A.R.: Global Optimization Test Problems (2011), http://www-optima.amp.i.kyoto-u.ac.jp/member/student/hedar/Hedar_files/TestGO.html (accessed December 22, 2011)

  18. Skolicki, Z., De Jong, K.: The Influence of Migration Sizes and Intervals on Island Models. In: 2005 Conference on Genetic and Evolutionary Computation (GECCO 2005), pp. 1295–1302. ACM (2005)

    Google Scholar 

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Hernández-Riveros, JA., Villada-Cano, D. (2012). Sensitivity Analysis of an Autonomous Evolutionary Algorithm. In: Pavón, J., Duque-Méndez, N.D., Fuentes-Fernández, R. (eds) Advances in Artificial Intelligence – IBERAMIA 2012. IBERAMIA 2012. Lecture Notes in Computer Science(), vol 7637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34654-5_28

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  • DOI: https://doi.org/10.1007/978-3-642-34654-5_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34653-8

  • Online ISBN: 978-3-642-34654-5

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