Optimization of biogas production with computational intelligence a comparative study. - Jörg Ziegenhirt; Thomas Bartz-Beielstein; Oliver Flasch; Wolfgang Konen; Martin Zaefferer (IEEE Congress on Evolutionary Computation, 2010)

Noisy optimization with sequential parameter optimization and optimal computational budget allocation. - Thomas Bartz-Beielstein; Martina Friese; Martin Zaefferer; Boris Naujoks; Oliver Flasch; Wolfgang Konen; Patrick Koch (GECCO (Companion), 2011)

SPOT applied to non-stochastic optimization problems: an experimental study. - Thomas Bartz-Beielstein; Martina Friese; Boris Naujoks; Martin Zaefferer (GECCO (Companion), 2012)

Sequential parameter optimization for symbolic regression. - Thomas Bartz-Beielstein; Oliver Flasch; Martin Zaefferer (GECCO (Companion), 2012)

Statistical analysis of optimization algorithms with R. - Thomas Bartz-Beielstein; Mike Preuß; Martin Zaefferer (GECCO (Companion), 2012)

Multi-criteria optimization for hard problems under limited budgets. - Martin Zaefferer; Thomas Bartz-Beielstein; Martina Friese; Boris Naujoks; Oliver Flasch (GECCO (Companion), 2012)

A Case Study on Multi-Criteria Optimization of an Event Detection Software under Limited Budgets. - Martin Zaefferer; Thomas Bartz-Beielstein; Boris Naujoks; Tobias Wagner 001; Michael Emmerich 001 (EMO, 2013)

Comparing Ensemble-Based Forecasting Methods for Smart-Metering Data. - Oliver Flasch; Martina Friese; Katya Vladislavleva; Thomas Bartz-Beielstein; Olaf Mersmann; Boris Naujoks; Jörg Stork; Martin Zaefferer (EvoApplications, 2013)

How to create meaningful and generalizable results. - Thomas Bartz-Beielstein; Martin Zaefferer; Boris Naujoks (GECCO (Companion), 2013)

Tuning multi-objective optimization algorithms for cyclone dust separators. - Martin Zaefferer; Beate Breiderhoff; Boris Naujoks; Martina Friese; Jörg Stork; Andreas Fischbach; Oliver Flasch; Thomas Bartz-Beielstein (GECCO, 2014)

Efficient global optimization for combinatorial problems. - Martin Zaefferer; Jörg Stork; Martina Friese; Andreas Fischbach; Boris Naujoks; Thomas Bartz-Beielstein (GECCO, 2014)

Distance Measures for Permutations in Combinatorial Efficient Global Optimization. - Martin Zaefferer; Jörg Stork; Thomas Bartz-Beielstein (PPSN, 2014)

Comparison of different Methods for Univariate Time Series Imputation in R. - Steffen Moritz; Alexis Sardá; Thomas Bartz-Beielstein; Martin Zaefferer; Jörg Stork (CoRR, 2015)

Tutorials at PPSN 2016. - Carola Doerr; Nicolas Bredèche; Enrique Alba 001; Thomas Bartz-Beielstein; Dimo Brockhoff; Benjamin Doerr; Gusz Eiben; Michael G. Epitropakis; Carlos M. Fonseca; Andreia P. Guerreiro; Evert Haasdijk; Jacqueline Heinerman; et al. (PPSN, 2016)

Efficient Global Optimization with Indefinite Kernels. - Martin Zaefferer; Thomas Bartz-Beielstein (PPSN, 2016)

Multi-fidelity modeling and optimization of biogas plants. - Martin Zaefferer; Daniel Gaida; Thomas Bartz-Beielstein (Appl. Soft Comput., 2016)

Simulation-based test functions for optimization algorithms. - Martin Zaefferer; Andreas Fischbach; Boris Naujoks; Thomas Bartz-Beielstein (GECCO, 2017)

Model-based methods for continuous and discrete global optimization. - Thomas Bartz-Beielstein; Martin Zaefferer (Appl. Soft Comput., 2017)

In a Nutshell: Sequential Parameter Optimization. - Thomas Bartz-Beielstein; Lorenzo Gentile; Martin Zaefferer (CoRR, 2017)

Surrogate assisted optimization of particle reinforced metal matrix composites. - Lorenzo Gentile; Martin Zaefferer; Dario Giugliano; Haofeng Chen; Thomas Bartz-Beielstein (GECCO, 2018)

Comparison of parallel surrogate-assisted optimization approaches. - Frederik Rehbach; Martin Zaefferer; Jörg Stork; Thomas Bartz-Beielstein (GECCO, 2018)

A First Analysis of Kernels for Kriging-Based Optimization in Hierarchical Search Spaces. - Martin Zaefferer; Daniel Horn (PPSN (2), 2018)

Linear Combination of Distance Measures for Surrogate Models in Genetic Programming. - Martin Zaefferer; Jörg Stork; Oliver Flasch; Thomas Bartz-Beielstein (PPSN (2), 2018)

Distance-based Kernels for Surrogate Model-based Neuroevolution. - Jörg Stork; Martin Zaefferer; Thomas Bartz-Beielstein (CoRR, 2018)

Surrogate models for discrete optimization problems - Martin Zaefferer (2018)

Improving NeuroEvolution Efficiency by Surrogate Model-Based Optimization with Phenotypic Distance Kernels. - Jörg Stork; Martin Zaefferer; Thomas Bartz-Beielstein (EvoApplications, 2019)

Prediction of neural network performance by phenotypic modeling. - Alexander Hagg; Martin Zaefferer; Jörg Stork; Adam Gaier (GECCO (Companion), 2019)

Surrogates for hierarchical search spaces: the wedge-kernel and an automated analysis. - Daniel Horn; Jörg Stork; Nils-Jannik Schüßler; Martin Zaefferer (GECCO, 2019)

Surrogate models for enhancing the efficiency of neuroevolution in reinforcement learning. - Jörg Stork; Martin Zaefferer; Thomas Bartz-Beielstein; A. E. Eiben (GECCO, 2019)

An empirical approach for probing the definiteness of kernels. - Martin Zaefferer; Thomas Bartz-Beielstein; Günter Rudolph (Soft Comput., 2019)

Understanding the Behavior of Reinforcement Learning Agents. - Jörg Stork; Martin Zaefferer; Thomas Bartz-Beielstein; A. E. Eiben (BIOMA, 2020)

Expected improvement versus predicted value in surrogate-based optimization. - Frederik Rehbach; Martin Zaefferer; Boris Naujoks; Thomas Bartz-Beielstein (GECCO, 2020)

Continuous Optimization Benchmarks by Simulation. - Martin Zaefferer; Frederik Rehbach (PPSN (1), 2020)

Simulation of an Elevator Group Control Using Generative Adversarial Networks and Related AI Tools. - Tom Peetz; Sebastian Vogt; Martin Zaefferer; Thomas Bartz-Beielstein (CoRR, 2020)

Towards Better Integration of Surrogate Models and Optimizers. - Tinkle Chugh; Alma A. M. Rahat; Vanessa Volz; Martin Zaefferer (High-Performance Simulation-Based Optimization, 2020)

Open Issues in Surrogate-Assisted Optimization. - Jörg Stork; Martina Friese; Martin Zaefferer; Thomas Bartz-Beielstein; Andreas Fischbach; Beate Breiderhoff; Boris Naujoks; Tea Tusar (High-Performance Simulation-Based Optimization, 2020)

Optimization and Adaptation of a Resource Planning Tool for Hospitals Under Special Consideration of the COVID-19 Pandemic. - Thomas Bartz-Beielstein; Marcel Dröscher; Alpar Gür; Alexander Hinterleitner; Tom Lawton; Olaf Mersmann; Dessislava Peeva; Lennard Reese; Nicolas Rehbach; Frederik Rehbach; Amrita Sen; Aleksandr Subbotin; et al. (CEC, 2021)

Resource planning for hospitals under special consideration of the COVID-19 pandemic: optimization and sensitivity analysis. - Thomas Bartz-Beielstein; Marcel Dröscher; Alpar Gür; Alexander Hinterleitner; Olaf Mersmann; Dessislava Peeva; Lennard Reese; Nicolas Rehbach; Frederik Rehbach; A. Sen; Aleksandr Subbotin; Martin Zaefferer (GECCO Companion, 2021)

Behavior-based neuroevolutionary training in reinforcement learning. - Jörg Stork; Martin Zaefferer; Nils Eisler; Patrick Tichelmann; Thomas Bartz-Beielstein; A. E. Eiben (GECCO Companion, 2021)

Resource Planning for Hospitals Under Special Consideration of the COVID-19 Pandemic: Optimization and Sensitivity Analysis. - Thomas Bartz-Beielstein; Marcel Dröscher; Alpar Gür; Alexander Hinterleitner; Olaf Mersmann; Dessislava Peeva; Lennard Reese; Nicolas Rehbach; Frederik Rehbach; Amrita Sen; Aleksandr Subbotin; Martin Zaefferer (CoRR, 2021)

Experimental Investigation and Evaluation of Model-based Hyperparameter Optimization. - Eva Bartz; Martin Zaefferer; Olaf Mersmann; Thomas Bartz-Beielstein (CoRR, 2021)

Underwater Acoustic Networks for Security Risk Assessment in Public Drinking Water Reservoirs. - Jörg Stork; Philip Wenzel; Severin Landwein; María-Elena Algorri; Martin Zaefferer; Wolfgang Kusch; Martin Staubach; Thomas Bartz-Beielstein; Hartmut Köhn; Hermann Dejager; Christian Wolf 003 (CoRR, 2021)

The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems. - Laurens Bliek; Paulo da Costa 001; Reza Refaei Afshar; Yingqian Zhang 001; Tom Catshoek; Daniël Vos; Sicco Verwer; Fynn Schmitt-Ulms; André Hottung; Tapan Shah 001; Meinolf Sellmann; Kevin Tierney; et al. (CoRR, 2022)

Benchmark-Driven Configuration of a Parallel Model-Based Optimization Algorithm. - Frederik Rehbach; Martin Zaefferer; Andreas Fischbach; Günter Rudolph; Thomas Bartz-Beielstein (IEEE Trans. Evol. Comput., 2022)

Case Study I: Tuning Random Forest (Ranger). - Thomas Bartz-Beielstein; Sowmya Chandrasekaran; Frederik Rehbach; Martin Zaefferer (Hyperparameter Tuning for Machine and Deep Learning with R, 2023)

Models. - Thomas Bartz-Beielstein; Martin Zaefferer (Hyperparameter Tuning for Machine and Deep Learning with R, 2023)

Hyperparameter Tuning Approaches. - Thomas Bartz-Beielstein; Martin Zaefferer (Hyperparameter Tuning for Machine and Deep Learning with R, 2023)

Tuning: Methodology. - Thomas Bartz-Beielstein; Martin Zaefferer; Olaf Mersmann (Hyperparameter Tuning for Machine and Deep Learning with R, 2023)

Case Study IV: Tuned Reinforcement Learning (in Python). - Martin Zaefferer; Sowmya Chandrasekaran (Hyperparameter Tuning for Machine and Deep Learning with R, 2023)

Global Study: Influence of Tuning. - Martin Zaefferer; Olaf Mersmann; Thomas Bartz-Beielstein (Hyperparameter Tuning for Machine and Deep Learning with R, 2023)

The first AI4TSP competition: Learning to solve stochastic routing problems. - Yingqian Zhang 001; Laurens Bliek; Paulo da Costa 001; Reza Refaei Afshar; Robbert Reijnen; Tom Catshoek; Daniël Vos; Sicco Verwer; Fynn Schmitt-Ulms; André Hottung; Tapan Shah 001; Meinolf Sellmann; et al. (Artif. Intell., 2023)

Quantum computer based feature selection in machine learning. - Gerhard Hellstern; Vanessa Dehn; Martin Zaefferer (IET Quantum Commun., 2024)