A simple modification in CMA-ES achieving linear time and space complexity

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

Volumn 5199 LNCS, Issue , 2008, Pages 296-305

  Ros, Raymond ^a   Hansen, Nikolaus ^b  

^a UNIVERSITÉ PARIS SACLAY   (France)

^b Parc Orsay Université   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BIOELECTRIC PHENOMENA; EVOLUTIONARY ALGORITHMS; FUNCTION EVALUATION; FUNCTIONS; LEARNING ALGORITHMS; PROBABILITY DENSITY FUNCTION; PROBLEM SOLVING; VECTORS; MATRIX ALGEBRA; OPTIMIZATION;

COVARIANCE MATRIX ADAPTATION EVOLUTION STRATEGIES; LEARNING RATES; LINEAR TIMES; MODEL COMPLEXITIES; OBJECTIVE FUNCTIONS; SIMPLE MODIFICATIONS; SPACE COMPLEXITIES;

COVARIANCE MATRIX;

COVARIANCE MATRIX ADAPTATION EVOLUTION STRATEGIES; HIGH-DIMENSIONAL; LEARNING RATES; MODEL COMPLEXITY; OBJECTIVE FUNCTIONS; ROSENBROCK FUNCTIONS; SIMPLE MODIFICATIONS; TIME AND SPACE COMPLEXITY;

EID: 56449130100     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-87700-4_30     Document Type: Conference Paper

References (10)

1. Arnold, D., Salomon, R.: Evolutionary gradient search revisited. IEEE Transactions on Evolutionary Computation 11(4), 480-495 (2007)
2. Hansen, N.: The CMA evolution strategy: a comparing review. In: Lozano, J., Larrañaga, P., Inza, I., Bengoetxea, E. (eds.) Towards a new evolutionary computation. Advances on estimation of distribution algorithms, pp. 75-102. Springer, Heidelberg (2006)
3. Hansen, N., Müller, S.D., Koumoutsakos, P.: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation. Evolutionary Computation 11(1), 1-18 (2003)
4. Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evolutionary computation 9(2), 159-195 (2001)
5. th International Conference on Genetic Algorithms, pp. 57-64. Morgan Kaufmann, San Francisco (1995)
6. Knight, J.N., Lunacek, M.: Reducing the space-time complexity of the CMA-ES. In: GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 658-665. ACM Press, New York (2007)
7. Knight, J.N., Lunacek, M.: Reducing the space-time complexity of the CMA-ES: Addendum. In: Errata for [6] (March 2008), http://www.cs.colostate.edu/ ∼nate/lcmaes/errata.pdf
8. Ostermeier, A., Gawelczyk. A., Hansen, N.: Step-size Adaptation Based on Non-local Use of Selection Information. In: Davidor, Y, Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 189-198. Springer, Heidelberg (1994)
9. Poland, J., Zell, A.: Main vector adaptation: A CMA variant with linear time and space complexity. In: Spector, L., Goodman, E.D., Wu, A., Langdon, W.B., Voigt, H.-M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H., Burke, E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pp. 7-11. Morgan Kaufmann, San Francisco (2001)
10. Ros, R., Hansen, N.: A simple modification in CMA-ES achieving linear time and space complexity. Research Report 6498, INRIA (April 2008), http://hal.inria.fr/inria-00270901/en