Z. Pitra

发表

Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy

T. Frodl, J. Hlinka, F. Španiel, 2018, BMC Psychiatry.

Knowledge-based Selection of Gaussian Process Surrogates

M. Holeňa, L. Bajer, Z. Pitra, 2019 .

How to down-weight observations in robust regression: A metalearning study

J. Kalina, Z. Pitra, 2018 .

Landscape Analysis for Surrogate Models in the Evolutionary Black-Box Context

M. Holeňa, Z. Pitra, J. Koza, 2022, ArXiv.

Adaptive Selection of Gaussian Process Model for Active Learning in Expensive Optimization

Martin Holena, Jakub Repický, Zbynek Pitra, 2018, IAL@PKDD/ECML.

Landscape analysis of gaussian process surrogates for the covariance matrix adaptation evolution strategy

Martin Holena, Jakub Repický, Zbynek Pitra, 2019, GECCO.

Control system design methodology

Zbyněk Pitra, Z. Pitra, 1991 .

Doubly Trained Evolution Control for the Surrogate CMA-ES

Martin Holena, Zbynek Pitra, Lukás Bajer, 2016, PPSN.

Interaction between model and its evolution control in surrogate-assisted CMA evolution strategy

Martin Holena, Zbynek Pitra, Jirí Tumpach, 2021, GECCO.

Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models

Martin Holena, Jakub Repický, Zbynek Pitra, 2017, ITAT.

Overview of surrogate-model versions of covariance matrix adaptation evolution strategy

Martin Holena, Jakub Repický, Zbynek Pitra, 2017, GECCO.

Comparison of ordinal and metric gaussian process regression as surrogate models for CMA evolution strategy

Martin Holena, Jakub Repický, Zbynek Pitra, 2017, GECCO.

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

Martin Holena, Zbynek Pitra, Lukás Bajer, 2015, GECCO.

Investigation of Gaussian Processes and Random Forests as Surrogate Models for Evolutionary Black-Box Optimization

Martin Holena, Zbynek Pitra, Lukás Bajer, 2015, GECCO.

Assessment of Surrogate Model Settings Using Landscape Analysis

Martin Holena, Zbynek Pitra, Mikulás Dvorák, 2020, ITAT.

Towards Landscape Analysis in Adaptive Learning of Surrogate Models

Martin Holena, Zbynek Pitra, M. Holeňa, 2020, IAL@PKDD/ECML.

Boosted Regression Forest for the Doubly Trained Surrogate Covariance Matrix Adaptation Evolution Strategy

Martin Holeňa, Jakub Repický, Zbyněk Pitra, 2018, ITAT.

Transfer of Knowledge for Surrogate Model Selection in Cost-Aware Optimization

Martin Holena, Jakub Repický, Zbynek Pitra, 2018, IAL@PKDD/ECML.

Automated Selection of Covariance Function for Gaussian process Surrogate Models

Martin Holena, Jakub Repický, Zbynek Pitra, 2018, ITAT.

Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy

Martin Holeňa, Jakub Repický, Lukáš Bajer, 2017, ITAT.

Comparing SVM, Gaussian Process and Random Forest Surrogate Models for the CMA-ES

Martin Holena, Zbynek Pitra, Lukás Bajer, 2015, ITAT.

Gaussian Process Surrogate Models for the CMA Evolution Strategy

M. Holeňa, L. Bajer, Z. Pitra, 2019, Evolutionary Computation.