Benchmarking Gaussian Processes and Random Forests Surrogate Models on the BBOB Noiseless Testbed. - Lukás Bajer; Zbynek Pitra; Martin Holena (GECCO (Companion), 2015)

Investigation of Gaussian Processes and Random Forests as Surrogate Models for Evolutionary Black-Box Optimization. - Lukás Bajer; Zbynek Pitra; Martin Holena (GECCO (Companion), 2015)

Investigation of Gaussian Processes in the Context of Black-Box Evolutionary Optimization. - Andrej Kudinov; Lukás Bajer; Zbynek Pitra; Martin Holena (ITAT, 2015)

Comparing SVM, Gaussian Process and Random Forest Surrogate Models for the CMA-ES. - Zbynek Pitra; Lukás Bajer; Martin Holena (ITAT, 2015)

Doubly Trained Evolution Control for the Surrogate CMA-ES. - Zbynek Pitra; Lukás Bajer; Martin Holena (PPSN, 2016)

Ordinal versus metric gaussian process regression in surrogate modelling for CMA evolution strategy. - Zbynek Pitra; Lukás Bajer; Jakub Repický; Martin Holena (GECCO (Companion), 2017)

Overview of surrogate-model versions of covariance matrix adaptation evolution strategy. - Zbynek Pitra; Lukás Bajer; Jakub Repický; Martin Holena (GECCO (Companion), 2017)

Comparison of ordinal and metric gaussian process regression as surrogate models for CMA evolution strategy. - Zbynek Pitra; Lukás Bajer; Jakub Repický; Martin Holena (GECCO (Companion), 2017)

Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy. - Zbynek Pitra; Lukás Bajer; Jakub Repický; Martin Holena (ITAT, 2017)

Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models. - Jakub Repický; Lukás Bajer; Zbynek Pitra; Martin Holena (ITAT, 2017)

Boosted Regression Forest for the Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy. - Zbynek Pitra; Jakub Repický; Martin Holena (ITAT, 2018)

Automated Selection of Covariance Function for Gaussian process Surrogate Models. - Jakub Repický; Martin Holena; Zbynek Pitra (ITAT, 2018)

Transfer of Knowledge for Surrogate Model Selection in Cost-Aware Optimization. - Zbynek Pitra; Jakub Repický; Martin Holena (IAL@PKDD/ECML, 2018)

Adaptive Selection of Gaussian Process Model for Active Learning in Expensive Optimization. - Jakub Repický; Zbynek Pitra; Martin Holena (IAL@PKDD/ECML, 2018)

Gaussian process surrogate models for the CMA-ES. - Lukás Bajer; Zbynek Pitra; Jakub Repický; Martin Holena (GECCO (Companion), 2019)

Landscape analysis of gaussian process surrogates for the covariance matrix adaptation evolution strategy. - Zbynek Pitra; Jakub Repický; Martin Holena (GECCO, 2019)

Gaussian Process Surrogate Models for the CMA Evolution Strategy. - Lukás Bajer; Zbynek Pitra; Jakub Repický; Martin Holena (Evol. Comput., 2019)

Assessment of Surrogate Model Settings Using Landscape Analysis. - Mikulás Dvorák; Zbynek Pitra; Martin Holena (ITAT, 2020)

Towards Landscape Analysis in Adaptive Learning of Surrogate Models. - Zbynek Pitra; Martin Holena (IAL@PKDD/ECML, 2020)

Interaction between model and its evolution control in surrogate-assisted CMA evolution strategy. - Zbynek Pitra; Marek Hanus; Jan Koza; Jirí Tumpach; Martin Holena (GECCO, 2021)

Combining Gaussian Processes and Neural Networks in Surrogate Modelling for Covariance Matrix Adaptation Evolution Strategy. - Jan Koza; Jirí Tumpach; Zbynek Pitra; Martin Holena (ITAT, 2021)

Using Past Experience for Configuration of Gaussian Processes in Black-Box Optimization. - Jan Koza; Jirí Tumpach; Zbynek Pitra; Martin Holena (LION, 2021)

Combining Gaussian Processes with Neural Networks for Active Learning in Optimization. - Jirí Ruzicka; Jan Koza; Jirí Tumpach; Zbynek Pitra; Martin Holena (IAL@PKDD/ECML, 2021)

Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context. - Zbynek Pitra; Jan Koza; Jirí Tumpach; Martin Holena (Evol. Comput., 2025)