Abstract
Motivated by parallel optimization, we experiment EDA-like adaptation-rules in the case of λ large. The rule we use, essentially based on estimation of multivariate normal algorithm, is (i) compliant with all families of distributions for which a density estimation algorithm exists (ii) simple (iii) parameter-free (iv) better than current rules in this framework of λ large. The speed-up as a function of λ is consistent with theoretical bounds.
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References
Baluja, S.: Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning, Technical Report CMU-CS-94-163, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA (1994)
Beyer, H.-G.: The Theory of Evolution Strategies. Natural Computing Series. Springer, Heidelberg (2001)
Beyer, H.-G.: The Theory of Evolutions Strategies. Springer, Heidelberg (2001)
Beyer, H.-G., Sendhoff, B.: Covariance matrix adaptation revisited - the CMSA evolution strategy. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 123–132. Springer, Heidelberg (2008)
Cai, Y., Sun, X., Xu, H., Jia, P.: Cross entropy and adaptive variance scaling in continuous eda. In: GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 609–616. ACM Press, New York (2007)
Donga, W., Yao, X.: Unified eigen analysis on multivariate gaussian based estimation of distribution algorithms. Information Sciences 178(15), 3000–3023 (2008)
Grahl, J., Bosman, P.A., Rothlauf, F.: The correlation-triggered adaptive variance scaling idea. In: GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 397–404. ACM, New York (2006)
Hansen, N.: Verallgemeinerte individuelle Schrittweitenregelung in der Evolutionsstrategie. Eine Untersuchung zur entstochastisierten,koordinatensystemunabhängigen Adaptation der Mutationsverteilung. Mensch und Buch Verlag, Berlin (1998) ISBN 3-933346-29-0
Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 9(2), 159–195 (2001)
Harik, G.R., Lobo, F.G., Goldberg, D.E.: The compact genetic algorithm. IEEE Trans. on Evolutionary Computation 3(4), 287 (1999)
Larranaga, P., Lozano, J.A.: Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Dordrecht (2001)
Liu, J., Teng, H.-F.: Model learning and variance control in continuous edas using pca. In: ICICIC 2008: Proceedings of the 2008 3rd International Conference on Innovative Computing Information and Control, Washington, DC, USA, p. 555. IEEE Computer Society, Los Alamitos (2008)
Mühlenbein, H., Höns, R.: The estimation of distributions and the minimum relative entropy principle. Evolutionary Computation 13(1), 1–27 (2005)
Mühlenbein, H., Mahnig, T.: Evolutionary computation and Wright’s equation. Theoretical Computer Science 287(1), 145–165 (2002)
Posík, P.: Preventing premature convergence in a simple eda via global step size setting. In: Rudolph, et al. (ed.) [18], pp. 549–558
Rechenberg, I.: Evolutionstrategie: Optimierung Technischer Systeme nach Prinzipien des Biologischen Evolution. Fromman-Holzboog Verlag, Stuttgart (1973)
Ros, R., Hansen, N.: A simple modification in cma-es achieving linear time and space complexity. In: Rudolph, et al. (ed.) [18], pp. 296–305
Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.): PPSN 2008. LNCS, vol. 5199. Springer, Heidelberg (2008)
Schwefel, H.-P.: Adaptive Mechanismen in der biologischen Evolution und ihr Einfluss auf die Evolutionsgeschwindigkeit. Interner Bericht der Arbeitsgruppe Bionik und Evolutionstechnik am Institut für Mess- und Regelungstechnik Re 215/3, Technische Universität Berlin (July 1974)
Shapiro, J.L.: Drift and scaling in estimation of distribution algorithms. Evolutionary Computation 13(1) (2005)
Teytaud, O., Fournier, H.: Lower bounds for evolution strategies using vc-dimension. In: Rudolph, et al. (ed.) [18], pp. 102–111.
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Teytaud, F., Teytaud, O. (2009). On the Parallel Speed-Up of Estimation of Multivariate Normal Algorithm and Evolution Strategies. In: Giacobini, M., et al. Applications of Evolutionary Computing. EvoWorkshops 2009. Lecture Notes in Computer Science, vol 5484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01129-0_75
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DOI: https://doi.org/10.1007/978-3-642-01129-0_75
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