Abstract
We employ local meta-models to enhance the efficiency of evolution strategies in the optimization of computationally expensive problems. The method involves the combination of second order local regression meta-models with the Covariance Matrix Adaptation Evolution Strategy. Experiments on benchmark problems demonstrate that the proposed meta-models have the potential to reliably account for the ranking of the offspring population resulting in significant computational savings. The results show that the use of local meta-models significantly increases the efficiency of already competitive evolution strategies.
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References
Jin, Y.: A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing 9(1), 3–12 (2005)
Büche, D., Schraudolph, N.N., Koumoutsakos, P.: Accelerating evolutionary algorithms with Gaussian process fitness function models. IEEE Trans.Sys., Man Cyber. 35(2), 183–194 (2005)
Huang, D., Allen, T.T., Notz, W.I., Zeng, N.: Global optimization of stochastic black-box systems via sequential kriging meta-models. J.Glob.Opt. 34(3), 441–466 (2006)
Jin, Y., Olhofer, M., Sendhoff, B.: A framework fo evolutionary optimization with approximate fitness functions. IEEE Trans. Evol. Comput. 6(5), 481–494 (2002)
Branke, J., Schmidt, C., Schmeck, H.: Efficient fitness estimation in noisy environments. In: Spector, J. (ed.) Genetic and Evolutionary Computation, pp. 243–250. Morgan Kaufmann, San Francisco (2001)
Regis, R.G., Shoemaker, C.A.: Local function approximation in evolutionary algorithms for the optimization of costly functions. IEEE Trans. Evol. Comput. 8(5), 490–504 (2004)
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. 401–410. Springer, Heidelberg (2004)
Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9(2), 159–195 (2001)
Hansen, N., Kern, S.: Evaluating the CMA evolution strategy on multimodal test functions. 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. 282–291. Springer, Heidelberg (2004)
Auger, A., Hansen, N.: A restart CMA evolution strategy with increasing population size. In: Proceedings of the IEEE Congress on Evolutionary Computation (2005)
Jin, Y., Hüsken, M., Sendhoff, B.: Quality measures for approximate models in evolutionary computation. In: Barry, A.M. (ed.) GECCO 2003. Proceedings of the Bird of a Feather Workshop, Genetic and Evolutionary Computation Conference, pp. 170–173. AAAI, Menlo Park (2003)
Atkeson, C.G., Moore, A.W., Schaal, S.: Locally weighted learning. Artificial Intelligence Review 11(1-5), 11–73 (1997)
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Kern, S., Hansen, N., Koumoutsakos, P. (2006). Local Meta-models for Optimization Using Evolution Strategies. In: Runarsson, T.P., Beyer, HG., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds) Parallel Problem Solving from Nature - PPSN IX. PPSN 2006. Lecture Notes in Computer Science, vol 4193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11844297_95
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DOI: https://doi.org/10.1007/11844297_95
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