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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Grefenstette, J.J.: Optimization of Control Parameters for Genetic Algorithms. IEEE Transactions on Systems, Man, and Cybernetics 16(1), 122–128 (1986)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computation. Springer (2003)
Lobo, F.G., Lima, C.F., Michalewicz, Z. (eds.): Parameter Setting in Evolutionary Algorithms. Springer (2007)
Hernández, J.A., Ospina, J.D.: A Multi Dynamics Algorithm for Global Optimization. Mathematical and Computer Modelling 52(7), 1271–1278 (2010)
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)
Jihong, Y., Muammer, K., Jay, L.: A Prognostic Algorithm for Machine Performance Assessment and its Application. Production Planning & Control 15(8), 796–801 (2004)
Hosmer, D.W., Lemeshow, S.: Applied Logistic Regression. Wiley, New York (1989)
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)
Hansen, N.: The CMA Evolution Strategy A Comparing Review. Studies in Fuziness and Soft Computing 192, 75–102 (2006)
Eiben, A.E., Hinterding, R., Michalewicz, Z.: Parameter Control in Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation 3(2), 124–141 (1999)
Šprogar, M.: Autonomous Evolutionary Algorithm. In: Fuerstner, I. (ed.) Products and Services from R & D to Final Solutions. Sciyo (2010)
Hamadi, Y., Monfroy, E., Saubion, F.: What is Autonomous Search? Springer Optimization and its Applications 45, 357–391 (2010)
Bäck, T.: Introduction to the Special Issue ( Self-Adaptation. Evolutionary Computation 9(2), 3–4 (2001)
Yu, X., Gen, M.: Introduction to Evolutionary Algorithms. Springer, London (2010)
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)
Montgomery, D.C.: Introduction to Statistical Quality Control. Wiley (2008)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)