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Mirrored Sampling and Sequential Selection for Evolution Strategies

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Parallel Problem Solving from Nature, PPSN XI (PPSN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6238))

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Abstract

This paper reveals the surprising result that a single-parent non-elitist evolution strategy (ES) can be locally faster than the (1+1)-ES. The result is brought about by mirrored sampling and sequential selection. With mirrored sampling, two offspring are generated symmetrically or mirrored with respect to their parent. In sequential selection, the offspring are evaluated sequentially and the iteration is concluded as soon as one offspring is better than the current parent. Both concepts complement each other well. We derive exact convergence rates of the (1,λ)-ES with mirrored sampling and/or sequential selection on the sphere model. The log-linear convergence of the ES is preserved. Both methods lead to an improvement and in combination the (1,4)-ES becomes about 10% faster than the (1+1)-ES. Naively implemented into the CMA-ES with recombination, mirrored sampling leads to a bias on the step-size. However, the (1,4)-CMA-ES with mirrored sampling and sequential selection is unbiased and appears to be faster, more robust, and as local as the (1+1)-CMA-ES.

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References

  1. Arnold, D.V., Salomon, R.: Evolutionary gradient search revisited. IEEE Transactions on Evolutionary Computation 11(4), 480–495 (2007)

    Article  Google Scholar 

  2. Arnold, D.V., Van Wart, D.C.S.: Cumulative step length adaptation for evolution strategies using negative recombination weights. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Drechsler, R., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., McCormack, J., O’Neill, M., Romero, J., Rothlauf, F., Squillero, G., Uyar, A.Ş., Yang, S. (eds.) EvoWorkshops 2008. LNCS, vol. 4974, pp. 545–554. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  3. Auger, A., Brockhoff, D., Hansen, N.: Mirrored sampling and sequential selection for evolution strategies. Research Report RR-7249, INRIA Saclay—Île-de-France (June 2010)

    Google Scholar 

  4. Auger, A., Hansen, N.: Reconsidering the progress rate theory for evolution strategies in finite dimensions. In: Keijzer, et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006), pp. 445–452. ACM Press, New York (2006)

    Chapter  Google Scholar 

  5. Auger, A., Hansen, N.: Benchmarking the (1+1)-CMA-ES on the BBOB-2009 noisy testbed. In: Rothlauf, F., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2009), Companion Material, pp. 2467–2472. ACM Press, New York (2009)

    Google Scholar 

  6. Hansen, N.: An analysis of mutative σ-self-adaptation on linear fitness functions. Evolutionary Computation 14(3), 255–275 (2006)

    Article  Google Scholar 

  7. Hansen, N.: The CMA evolution strategy: a comparing review. In: Lozano, J., Larranaga, P., Inza, I., Bengoetxea, E. (eds.) Towards a New Evolutionary Computation. Advances on Estimation of Distribution Algorithms, pp. 75–102. Springer, Heidelberg (2006)

    Google Scholar 

  8. Hansen, N., Finck, S., Ros, R., Auger, A.: Real-parameter black-box optimization benchmarking 2009: Noiseless functions definitions. Tech. Rep. RR-6829, INRIA (2009), http://coco.gforge.inria.fr/bbob2010-downloads (updated February 2010)

  9. Hansen, N., Finck, S., Ros, R., Auger, A.: Real-parameter black-box optimization benchmarking 2009: Noisy functions definitions. Tech. Rep. RR-6869, INRIA (2009), http://coco.gforge.inria.fr/bbob2010-downloads (updated February 2010)

  10. Igel, C., Suttorp, T., Hansen, N.: A computational efficient covariance matrix update and a (1+1)-CMA for evolution strategies. In: Keijzer, et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006), pp. 453–460. ACM Press, New York (2006)

    Chapter  Google Scholar 

  11. Teytaud, O., Gelly, S., Mary, J.: On the ultimate convergence rates for isotropic algorithms and the best choices among various forms of isotropy. 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. 32–41. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

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Brockhoff, D., Auger, A., Hansen, N., Arnold, D.V., Hohm, T. (2010). Mirrored Sampling and Sequential Selection for Evolution Strategies. 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_2

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  • DOI: https://doi.org/10.1007/978-3-642-15844-5_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15843-8

  • Online ISBN: 978-3-642-15844-5

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