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Benchmarking Gaussian Processes and Random Forests Surrogate Models on the BBOB Noiseless Testbed

  1. 1.
    Bajer, Lukáš - Pitra, Z. - Holeňa, Martin
    Benchmarking Gaussian Processes and Random Forests Surrogate Models on the BBOB Noiseless Testbed.
    GECCO Companion '15. Genetic and Evolutionary Computation Conference. Companion Material Proceedings. New York: ACM, 2015 - (Silva, S.), s. 1143-1150. ISBN 978-1-4503-3488-4.
    [GECCO Companion '15. Genetic and Evolutionary Computation Conference. Madrid (ES), 11.07.2015-15.07.2015]
    http://hdl.handle.net/11104/0248874

    Cited: 5

    --- SINGH, P. - DWIVEDI, P. Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem. APPLIED ENERGY. ISSN 0306-2619, MAY 1 2018, vol. 217, p. 537-549. [WOS]
    --- TALBI, H. - DRAA, A. A new real-coded quantum-inspired evolutionary algorithm for continuous optimization. APPLIED SOFT COMPUTING. ISSN 1568-4946, DEC 2017, vol. 61, p. 765-791. [WOS]
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    --- SHARMA, B. - PRAKASH, R. - TIWARI, S. - MISHRA, K.K. A variant of environmental adaptation method with real parameter encoding and its application in economic load dispatch problem. APPLIED INTELLIGENCE. ISSN 0924-669X, SEP 2017, vol. 47, no. 2, p. 409-429. [WOS]
    --- SALEEM, S. - GALLAGHER, M. - WOOD, I. A Model-Based Framework for Black-Box Problem Comparison Using Gaussian Processes. PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XV, PT II. ISSN 0302-9743, 2018, vol. 11102, p. 284-295. [WOS]
Number of the records: 1  

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