- Model Guided Sampling Optimization with Gaussian Processes for Expensive Black-Box OptimizationPublication without faculty affiliation
- Comparing Non-Linear Regression Methods on Black-Box Optimization Benchmarks2015 | Faculty of Mathematics and Physics
- Investigation of Gaussian Processes and Random Forests As Surrogate Models for Evolutionary Black-Box Optimization2015 | Faculty of Mathematics and Physics
- Neural-network-based estimation of normal distributions in black-box optimization2022 | Faculty of Mathematics and Physics
- Testing Gaussian Process Surrogates on CEC'2013 multi-modal benchmark2016 | Faculty of Mathematics and Physics
- Traditional Gaussian Process Surrogates in the BBOB Framework2016 | Faculty of Mathematics and Physics
- Benchmarking Gaussian Processes and Random Forests Surrogate Models on the BBOB Noiseless Testbed2015 | Faculty of Mathematics and Physics
- Doubly Trained Evolution Control for the Surrogate CMA-ES2016 | Faculty of Mathematics and Physics
- Combining Gaussian Processes with Neural Networks for Active Learning in OptimizationPublication without faculty affiliation
- Interaction between Model and Its Evolution Control in Surrogate-Assisted CMA Evolution Strategy2021 | Faculty of Mathematics and Physics, Central Library of Charles University
- Improving the Model Guided Sampling Optimization by Model Search and Slice Sampling2013 | Faculty of Mathematics and Physics
- Towards Low-dimensional Gaussian Process Metamodels for CMA-ES2014 | Faculty of Mathematics and Physics
- Combining Gaussian processes and neural networks in surrogate modeling for covariance matrix adaptation evolution strategy2021 | Faculty of Mathematics and Physics, Central Library of Charles University