Abstract
Taking inspiration from approximate ranking, this paper investigates the use of rank-based Support Vector Machine as surrogate model within CMA-ES, enforcing the invariance of the approach with respect to monotonous transformations of the fitness function. Whereas the choice of the SVM kernel is known to be a critical issue, the proposed approach uses the Covariance Matrix adapted by CMA-ES within a Gaussian kernel, ensuring the adaptation of the kernel to the currently explored region of the fitness landscape at almost no computational overhead. The empirical validation of the approach on standard benchmarks, comparatively to CMA-ES and recent surrogate-based CMA-ES, demonstrates the efficiency and scalability of the proposed approach.
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References
Auger, A., Hansen, N., Perez Zerpa, J., Ros, R., Schoenauer, M.: Experimental comparisons of derivative free optimization algorithms. In: Vahrenhold, J. (ed.) 8th Intl Symp. Experimental Algorithms. LNCS, vol. 5526, pp. 3–15. Springer, Heidelberg (2009)
Barthelemy, J.-F.M., Haftka, R.: Approximation concepts for optimial structural design – a review. Structural Optimization 5, 129–144 (1993)
Bouzarkouna, Z., Auger, A., Ding, D.: Investigating the local-meta-model CMA-ES for large population sizes. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcazar, A.I., Goh, C.-K., Merelo, J.J., Neri, F., Preuß, M., Togelius, J., Yannakakis, G.N. (eds.) EvoApplicatons 2010. LNCS, vol. 6024, pp. 402–411. Springer, Heidelberg (2010)
Cristianini, N., Shawe-Taylor, J.: An introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
Gelly, S., Ruette, S., Teytaud, O.: Comparison-based algorithms are robust and randomized algorithms anytime. Evolutionary Computation 15(4), 411–434 (2007)
Hansen, N.: Adaptive encoding: How to render search coordinate system invariant. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 205–214. Springer, Heidelberg (2008)
Hansen, N., Auger, A., Ros, R., Finck, S., Pošík, P.: Comparing results of 31 algorithms from the BBOB-2009. In: GECCO Workshop Proc. ACM Press, New York (2010)
Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 9(2), 159–195 (2001)
Jin, Y.: A Comprehensive Survey of Fitness Approximation in Evolutionary Computation. Soft Computing 9(1), 3–12 (2005)
Joachims, T.: A support vector method for multivariate performance measures. In: Raedt, L.D., Wrobel, S. (eds.) Proc. ICML 2005. ACM International Conference Proceeding Series, vol. 119, pp. 377–384. ACM, New York (2005)
Kern, S., Hansen, N., Koumoutsakos, P.: Local meta-models for optimization using evolution strategies. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 939–948. Springer, Heidelberg (2006)
Rasheed, K., Hirsh, H.: Informed operators: Speeding up genetic-algorithm-based design optimization using reduced models. In: Whitley, D., et al. (eds.) GECCO 2000, pp. 628–635. Morgan Kaufmann, San Francisco (2000)
Runarsson, T.P.: Constrained evolutionary optimization by approximate ranking and surrogate models. 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. 401–408. Springer, Heidelberg (2004)
Runarsson, T.P.: Ordinal regression in evolutionary computation. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 1048–1057. Springer, Heidelberg (2006)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
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Loshchilov, I., Schoenauer, M., Sebag, M. (2010). Comparison-Based Optimizers Need Comparison-Based Surrogates. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15844-5_37
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DOI: https://doi.org/10.1007/978-3-642-15844-5_37
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