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ORCIDZbynek Pitra
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2020 – today
- 2022
[i2]Zbynek Pitra, Jan Koza, Jirí Tumpach, Martin Holena:
Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context. CoRR abs/2203.11315 (2022)- 2021
- [c22]Zbynek Pitra, Marek Hanus, Jan Koza, Jirí Tumpach
, Martin Holena:
Interaction between model and its evolution control in surrogate-assisted CMA evolution strategy. GECCO 2021: 528-536 - [c21]Jan Koza, Jirí Tumpach, Zbynek Pitra, Martin Holena:
Combining Gaussian Processes and Neural Networks in Surrogate Modelling for Covariance Matrix Adaptation Evolution Strategy. ITAT 2021: 29-38 - [c20]Jan Koza, Jirí Tumpach, Zbynek Pitra, Martin Holena:
Using Past Experience for Configuration of Gaussian Processes in Black-Box Optimization. LION 2021: 167-182 - [c19]Jirí Ruzicka, Jan Koza, Jirí Tumpach, Zbynek Pitra, Martin Holena:
Combining Gaussian Processes with Neural Networks for Active Learning in Optimization. IAL@PKDD/ECML 2021: 105-120 - 2020
- [c18]Mikulás Dvorák, Zbynek Pitra, Martin Holena:
Assessment of Surrogate Model Settings Using Landscape Analysis. ITAT 2020: 81-89 - [c17]Zbynek Pitra, Martin Holena:
Towards Landscape Analysis in Adaptive Learning of Surrogate Models. IAL@PKDD/ECML 2020: 78-83
2010 – 2019
- 2019
- [j1]Lukás Bajer, Zbynek Pitra, Jakub Repický, Martin Holena:
Gaussian Process Surrogate Models for the CMA Evolution Strategy. Evol. Comput. 27(4): 665-697 (2019) - [c16]Lukás Bajer, Zbynek Pitra, Jakub Repický, Martin Holena:
Gaussian process surrogate models for the CMA-ES. GECCO (Companion) 2019: 17-18 - [c15]Zbynek Pitra, Jakub Repický, Martin Holena:
Landscape analysis of gaussian process surrogates for the covariance matrix adaptation evolution strategy. GECCO 2019: 691-699 - 2018
- [c14]Jakub Repický, Martin Holena, Zbynek Pitra:
Automated Selection of Covariance Function for Gaussian process Surrogate Models. ITAT 2018: 64-71 - [c13]Zbynek Pitra, Jakub Repický, Martin Holena:
Boosted Regression Forest for the Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy. ITAT 2018: 72-79 - [c12]Jakub Repický, Zbynek Pitra, Martin Holena:
Adaptive Selection of Gaussian Process Model for Active Learning in Expensive Optimization. IAL@PKDD/ECML 2018: 80-84 - [c11]Zbynek Pitra, Jakub Repický, Martin Holena:
Transfer of Knowledge for Surrogate Model Selection in Cost-Aware Optimization. IAL@PKDD/ECML 2018: 89-94 - 2017
- [c10]Zbynek Pitra, Lukás Bajer, Jakub Repický, Martin Holena:
Ordinal versus metric gaussian process regression in surrogate modelling for CMA evolution strategy. GECCO (Companion) 2017: 177-178 - [c9]Zbynek Pitra, Lukás Bajer, Jakub Repický, Martin Holena:
Overview of surrogate-model versions of covariance matrix adaptation evolution strategy. GECCO (Companion) 2017: 1622-1629 - [c8]Zbynek Pitra, Lukás Bajer, Jakub Repický, Martin Holena:
Comparison of ordinal and metric gaussian process regression as surrogate models for CMA evolution strategy. GECCO (Companion) 2017: 1764-1771 - [c7]Zbynek Pitra, Lukás Bajer, Jakub Repický, Martin Holena:
Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy. ITAT 2017: 120-128 - [c6]Jakub Repický, Lukás Bajer, Zbynek Pitra, Martin Holena:
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models. ITAT 2017: 136-143 - [i1]Jakub Repický, Lukás Bajer, Zbynek Pitra, Martin Holena:
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models. CoRR abs/1709.10443 (2017) - 2016
- [c5]Zbynek Pitra, Lukás Bajer, Martin Holena:
Doubly Trained Evolution Control for the Surrogate CMA-ES. PPSN 2016: 59-68 - 2015
- [c4]Lukás Bajer, Zbynek Pitra, Martin Holena:
Benchmarking Gaussian Processes and Random Forests Surrogate Models on the BBOB Noiseless Testbed. GECCO (Companion) 2015: 1143-1150 - [c3]Lukás Bajer, Zbynek Pitra, Martin Holena:
Investigation of Gaussian Processes and Random Forests as Surrogate Models for Evolutionary Black-Box Optimization. GECCO (Companion) 2015: 1351-1352 - [c2]Andrej Kudinov, Lukás Bajer, Zbynek Pitra, Martin Holena:
Investigation of Gaussian Processes in the Context of Black-Box Evolutionary Optimization. ITAT 2015: 159-166 - [c1]Zbynek Pitra, Lukás Bajer, Martin Holena:
Comparing SVM, Gaussian Process and Random Forest Surrogate Models for the CMA-ES. ITAT 2015: 186-193
Coauthor Index
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