Bidirectional relation between CMA evolution strategies and natural evolution strategies

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6238 LNCS, Issue PART 1, 2010, Pages 154-163

  Akimoto, Youhei ^a,b   Nagata, Yuichi ^a   Ono, Isao ^a   Kobayashi, Shigenobu ^a  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

^b JAPAN SOCIETY FOR THE PROMOTION OF SCIENCE   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

CHOLESKY; COVARIANCE MATRIX ADAPTATION EVOLUTION STRATEGIES; EVOLUTION STRATEGIES; EXPLICIT FORM; MEAN VECTOR; MUTATION DISTRIBUTION; NATURAL EVOLUTION; NATURAL GRADIENT;

BIOLOGY; EVOLUTIONARY ALGORITHMS; PROBLEM SOLVING;

COVARIANCE MATRIX;

EID: 78149269859     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15844-5_16     Document Type: Conference Paper

References (17)

1. Wierstra, D., Schaul, T., Peters, J., Schmidhuber, J.: Natural evolution strategies. In: Proceedings of CEC 2008, pp. 3381-3387 (2008)
2. Sun, Y., Wierstra, D., Schaul, T., Schmidhuber, J.: Efficient natural evolution strategies. In: Proceedings of GECCO 2009, pp. 539-545 (2009)
3. Amari, S.: Natural gradient works efficiently in learning. Neural Computation 10(2), 251-276 (1998)
4. Hansen, N., Ostermeier, A.: Completely deranomized self-adaptation in evolution strategies. Evolutionary Computation 9(2), 159-195 (2001)
5. Hansen, N., Müller, S., Koumoutsakos, P.: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary Computation 11(1), 1-18 (2003) (Pubitemid 37044186)
6. Amari, S., Nagaoka, H.: Methods of Information Geometry. American Mathematical Society (2007)
7. Amari, S., Douglas, S.: Why natural gradient? In: Proceedings of ICASSP, pp. 1213-1216 (1998)
8. Harville, D.A.: Matrix Algebra From a Statistician's Perspective. Springer, Heidelberg (2008)
9. Murray, M., Rice, J.: Differential geometry and statistics. Chapman & Hall/CRC, Boca Raton (1993)
10. Suttorp, T., Hansen, N., Igel, C.: Efficient covariance matrix update for variable metric evolution strategies. Machine Learning 75(2), 167-197 (2009)
11. Peters, J., Schaal, S.: Natural actor-critic. Neurocomputing 71, 1180-1190 (2008)
12. Jastrebski, G.A., Arnold, D.V.: Improving evolution strategies through active covariance matrix adaptation. In: Proceedings of CEC 2006, pp. 9719-9726 (2006)
13. Kuan, C.M., Hornik, K.: Convergence of learning algorithms with constant learning rates. IEEE Transactions on Neural Networks 2(5), 484-489 (1991)
14. Kushner, H.J., Yin, G.G.: Stochastic approximation and recursive algorithms and applications. Springer, Heidelberg (2003)
15. Gonzàlez, A., Dorronsoro, J.R.: Natural conjugate gradient training of multilayer perceptrons. Neurocomputing 71(13-15), 2499-2506 (2008)
16. Honkela, A., Tornio, M., Raiko, T., Karhunen, J.: Natural conjugate gradient in variational inference. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds.) ICONIP 2007, Part II. LNCS, vol. 4985, pp. 305-314. Springer, Heidelberg (2008)
17. Nishimori, Y., Akaho, S., Plumbley, M.D.: Natural conjugate gradient on complex flag manifolds for complex independent subspace analysis. In: Kůrková, V., Neruda, R., Koutník, J. (eds.) ICANN 2008, Part I. LNCS, vol. 5163, pp. 165-174. Springer, Heidelberg (2008)