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Evolution Strategies with an RBM-Based Meta-Model

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Knowledge Management and Acquisition for Smart Systems and Services (PKAW 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8863))

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Abstract

Evolution strategies have been demonstrated to offer a state-of- the-art performance on different optimisation problems. The efficiency of the algorithm largely depends on its ability to build an adequate meta-model of the function being optimised. This paper proposes a novel algorithm RBM-ES that utilises a computationally efficient restricted Boltzmann machine for maintaining the meta-model. We demonstrate that our algorithm is able to adapt its model to complex multidimensional landscapes. Furthermore, we compare the proposed algorithm to state-of the art algorithms such as CMA-ES on different tasks and demonstrate that the RBM-ES can achieve good performance.

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References

  1. Auger, A., Brockhoff, D., Hansen, N.: Benchmarking the Local Metamodel CMA-ES on the Noiseless BBOB’2013 Test Bed. In: GECCO 2013 (2013)

    Google Scholar 

  2. Baluja, S., Caruana, R.: Removing the genetics from the standard genetic algorithm. In: ICML, pp. 1–11 (1995)

    Google Scholar 

  3. Sebag, M., Ducoulombier, A.: Extending population-based incremental learning to continuous search spaces. Parallel Probl. Solving from Nat. 5, 418–427 (1998)

    Article  Google Scholar 

  4. Branke, J., Schmidt, C., Schmec, H.: Efficient Fitness Estimation in Noisy Environments. In: Proceedings of Genetic and Evolutionary Computation (2001)

    Google Scholar 

  5. Gutmann, H.-M.: A Radial Basis Function Method for Global Optimization. J. Glob. Optim. 19, 201–227 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  6. Olhofer, M., Sendhoff, B.: A framework for evolutionary optimization with approximate fitness functions. IEEE Trans. Evol. Comput. 6, 481–494 (2002)

    Article  Google Scholar 

  7. Büche, D., Schraudolph, N.N., Koumoutsakos, P.: Accelerating Evolutionary Algorithms With Gaussian Process Fitness Function Models. IEEE Trans. Syst. Man, Cybern. Part C Appl. Rev. 35, 183–194 (2005)

    Article  Google Scholar 

  8. Runarsson, T.P.: Constrained evolutionary optimization by approximate ranking and Surrogate models. In: Yao, X., et al. (eds.) PPSN VIII. LNCS, vol. 3242, pp. 401–410. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  9. Emmerich, M., Giotis, A., Özdemir, M., Bäck, T., Giannakoglou, K.: Metamodel-Assisted Evolution Strategies. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN VII. LNCS, vol. 2439, pp. 361–370. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  10. Gallagher, M.: Black-box optimization benchmarking: Results for the BayEDAcG algorithm on the noiseless function testbed. In: GECCO 2009, p. 2383. ACM Press, New York (2009)

    Google Scholar 

  11. Forrester, A., Sobester, A., Keane, A.: Engineering Design via Surrogate Modelling A Practical Guide. Wiley (2008)

    Google Scholar 

  12. Hansen, N., Auger, A., Finck, S., Ros, R.: Real-Parameter Black-Box Optimization Benchmarking 2009: Experimental Setup (2009)

    Google Scholar 

  13. Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14, 1771–1800 (2002)

    Article  MATH  Google Scholar 

  14. Martens, J., Chattopadhyay, A., Pitassi, T., Zemel, R.: On the Representational Efficiency of Restricted Boltzmann Machines. In: NIPS, pp. 1–21 (2013)

    Google Scholar 

  15. Hinton, G.E.: To Recognize Shapes, First Learn to Generate Images. Prog. Brain Res. 165, 535–547 (2007)

    Article  Google Scholar 

  16. Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning. ACM Press (2010)

    Google Scholar 

  17. Hinton, G.E.: A Practical Guide to Training Restricted Boltzmann Machines (2010)

    Google Scholar 

  18. Desjardins, G., Courville, A.: Parallel tempering for training of restricted Boltzmann machines. In: AISTATS, pp. 145–152 (2010)

    Google Scholar 

  19. Larochelle, H., Mandel, M.: Learning Algorithms for the Classification Restricted Boltzmann Machine. J. Mach. Learn. Res. 13, 643–669 (2012)

    MATH  MathSciNet  Google Scholar 

  20. Desjardins, G., Bengio, Y.: Empirical evaluation of convolutional RBMs for vision (2008)

    Google Scholar 

  21. Luo, H., Shen, R., Niu, C.: Sparse Group Restricted Boltzmann Machines. Arxiv Prepr. arXiv1008.4988, pp. 1–9 (2010)

    Google Scholar 

  22. Cho, K., Ilin, A., Raiko, T.: Improved learning of Gaussian-Bernoulli restricted Boltzmann machines. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds.) ICANN 2011, Part I. LNCS, vol. 6791, pp. 10–17. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  23. Cho, K., Raiko, T., Ilin, A.: Enhanced gradient and adaptive learning rate for training restricted Boltzmann machines. Neural Computation 25(3) (2011)

    Google Scholar 

  24. Welling, M.: Product of Experts, http://www.scholarpedia.org/article/Product_of_experts

  25. Welling, M., Rosen-Zvi, M., Hinton, G.E.: Exponential family harmoniums with an application to information retrieval. In: Adv. Neural Inf. Process. Syst. (2005)

    Google Scholar 

  26. Wang, N., Melchior, J., Wiskott, L.: An analysis of Gaussian-binary restricted Boltzmann machines for natural images. In: ESANN (2012)

    Google Scholar 

  27. Bengio, Y.: Learning Deep Architectures for AI. Found. Trends Mach. Learn. 2, 1–127 (2009)

    Article  MATH  Google Scholar 

  28. Hansen, N., Auger, A., Ros, R., Finck, S., Pošík, P.: Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009. In: Proceedings of the 12th Annual Conference Comp. on Genetic and Evolutionary Computation, GECCO 2010, p. 1689. ACM Press, New York (2010)

    Chapter  Google Scholar 

  29. Hansen, N.: Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed. In: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference, GECCO 2009, p. 2389. ACM Press, New York (2009)

    Google Scholar 

  30. Taylor, M.E., Whiteson, S., Stone, P.: Comparing evolutionary and temporal difference methods in a reinforcement learning domain. In: GECCO 2006, p. 1321. ACM Press, New York (2006)

    Google Scholar 

  31. Heidrich-Meisner, V., Igel, C.: Similarities and differences between policy gradient methods and evolution strategies. In: ESANN, pp. 23–25 (2008)

    Google Scholar 

  32. Riedmiller, M., Peters, J., Schaal, S.: Evaluation of Policy Gradient Methods and Variants on the Cart-Pole Benchmark. In: 2007 IEEE ISADPRL, pp. 254–261 (2007)

    Google Scholar 

  33. Hansen, N.: The CMA Evolution Strategy: A Comparing Review. In: Lozano, J.A., Larrañaga, P., Inza, I., Bengoetxea, E. (eds.) Towards a New Evolutionary Computation. STUDFUZZ, vol. 192, pp. 75–102. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  34. Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press (2004)

    Google Scholar 

  35. Whitacre, J.M.: Adaptation and Self-Organization in Evolutionary Algorithms (2007)

    Google Scholar 

  36. Meyer-Nieberg, S., Beyer, H.: Self-adaptation in evolutionary algorithms. In: Lobo, F.G., Lima, C.F., Michalewicz, Z. (eds.) Parameter Setting in Evolutionary Algorithms. SCI, vol. 54, pp. 47–75. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. The University of Queensland, Brisbane, Australia

    Kirill Makukhin

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  1. Kirill Makukhin
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Editors and Affiliations

  1. University of Tasmania, Private Bag 87, 7001, Hobart, Tasmania, Australia

    Yang Sok Kim  & Byeong Ho Kang  & 

  2. Department of Computing, Faculty of Science, Macquarie University, 2109, Sydney, Australia

    Deborah Richards

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Makukhin, K. (2014). Evolution Strategies with an RBM-Based Meta-Model. In: Kim, Y.S., Kang, B.H., Richards, D. (eds) Knowledge Management and Acquisition for Smart Systems and Services. PKAW 2014. Lecture Notes in Computer Science(), vol 8863. Springer, Cham. https://doi.org/10.1007/978-3-319-13332-4_20

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  • DOI: https://doi.org/10.1007/978-3-319-13332-4_20

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13331-7

  • Online ISBN: 978-3-319-13332-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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