Martin Zaefferer

IET Quantum Commun., September, 2024

2023

The first AI4TSP competition: Learning to solve stochastic routing problems.

[BibT_eX]

[DOI]

Artif. Intell., June, 2023

Global Study: Influence of Tuning.

[BibT_eX]

[DOI]

Olaf Mersmann

Proceedings of the Hyperparameter Tuning for Machine and Deep Learning with R, 2023

Case Study IV: Tuned Reinforcement Learning (in Python).

[BibT_eX]

[DOI]

Sowmya Chandrasekaran

Proceedings of the Hyperparameter Tuning for Machine and Deep Learning with R, 2023

Tuning: Methodology.

[BibT_eX]

[DOI]

Olaf Mersmann

Proceedings of the Hyperparameter Tuning for Machine and Deep Learning with R, 2023

Hyperparameter Tuning Approaches.

[BibT_eX]

[DOI]

Proceedings of the Hyperparameter Tuning for Machine and Deep Learning with R, 2023

Models.

[BibT_eX]

[DOI]

Proceedings of the Hyperparameter Tuning for Machine and Deep Learning with R, 2023

Case Study I: Tuning Random Forest (Ranger).

[BibT_eX]

[DOI]

Sowmya Chandrasekaran

Proceedings of the Hyperparameter Tuning for Machine and Deep Learning with R, 2023

2022

Benchmark-Driven Configuration of a Parallel Model-Based Optimization Algorithm.

[BibT_eX]

[DOI]

IEEE Trans. Evol. Comput., 2022

The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems.

[BibT_eX]

[DOI]

CoRR, 2022

2021

Underwater Acoustic Networks for Security Risk Assessment in Public Drinking Water Reservoirs.

[BibT_eX]

[DOI]

Hartmut Köhn

Hermann Dejager

Christian Wolf

CoRR, 2021

Experimental Investigation and Evaluation of Model-based Hyperparameter Optimization.

[BibT_eX]

[DOI]

Eva Bartz

Olaf Mersmann

CoRR, 2021

Resource Planning for Hospitals Under Special Consideration of the COVID-19 Pandemic: Optimization and Sensitivity Analysis.

[BibT_eX]

[DOI]

Marcel Dröscher

Alpar Gür

Alexander Hinterleitner

CoRR, 2021

Behavior-based neuroevolutionary training in reinforcement learning.

[BibT_eX]

[DOI]

A. E. Eiben

Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

Resource planning for hospitals under special consideration of the COVID-19 pandemic: optimization and sensitivity analysis.

[BibT_eX]

[DOI]

Marcel Dröscher

Alpar Gür

Alexander Hinterleitner

Proceedings of the GECCO '21: Genetic and Evolutionary Computation Conference, 2021

Optimization and Adaptation of a Resource Planning Tool for Hospitals Under Special Consideration of the COVID-19 Pandemic.

[BibT_eX]

[DOI]

Marcel Dröscher

Alpar Gür

Alexander Hinterleitner

Proceedings of the IEEE Congress on Evolutionary Computation, 2021

2020

Open Issues in Surrogate-Assisted Optimization.

[BibT_eX]

[DOI]

Proceedings of the High-Performance Simulation-Based Optimization, 2020

Towards Better Integration of Surrogate Models and Optimizers.

[BibT_eX]

[DOI]

Proceedings of the High-Performance Simulation-Based Optimization, 2020

Simulation of an Elevator Group Control Using Generative Adversarial Networks and Related AI Tools.

[BibT_eX]

[DOI]

Tom Peetz

Sebastian Vogt

CoRR, 2020

Continuous Optimization Benchmarks by Simulation.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XVI, 2020

Expected improvement versus predicted value in surrogate-based optimization.

[BibT_eX]

[DOI]

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

Understanding the Behavior of Reinforcement Learning Agents.

[BibT_eX]

[DOI]

A. E. Eiben

Proceedings of the Bioinspired Optimization Methods and Their Applications, 2020

2019

An empirical approach for probing the definiteness of kernels.

[BibT_eX]

[DOI]

Günter Rudolph

Soft Comput., 2019

Surrogate models for enhancing the efficiency of neuroevolution in reinforcement learning.

[BibT_eX]

[DOI]

A. E. Eiben

Proceedings of the Genetic and Evolutionary Computation Conference, 2019

Surrogates for hierarchical search spaces: the wedge-kernel and an automated analysis.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2019

Prediction of neural network performance by phenotypic modeling.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

Improving NeuroEvolution Efficiency by Surrogate Model-Based Optimization with Phenotypic Distance Kernels.

[BibT_eX]

[DOI]

Proceedings of the Applications of Evolutionary Computation, 2019

2018

Surrogate models for discrete optimization problems

[BibT_eX]

[DOI]

PhD thesis, 2018

Distance-based Kernels for Surrogate Model-based Neuroevolution.

[BibT_eX]

[DOI]

CoRR, 2018

Linear Combination of Distance Measures for Surrogate Models in Genetic Programming.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XV, 2018

A First Analysis of Kernels for Kriging-Based Optimization in Hierarchical Search Spaces.

[BibT_eX]

[DOI]

Daniel Horn

Proceedings of the Parallel Problem Solving from Nature - PPSN XV, 2018

Comparison of parallel surrogate-assisted optimization approaches.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2018

Surrogate assisted optimization of particle reinforced metal matrix composites.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2018

2017

In a Nutshell: Sequential Parameter Optimization.

[BibT_eX]

[DOI]

Lorenzo Gentile

CoRR, 2017

Model-based methods for continuous and discrete global optimization.

[BibT_eX]

[DOI]

Appl. Soft Comput., 2017

Simulation-based test functions for optimization algorithms.

[BibT_eX]

[DOI]

Andreas Fischbach

Proceedings of the Genetic and Evolutionary Computation Conference, 2017

2016

Multi-fidelity modeling and optimization of biogas plants.

[BibT_eX]

[DOI]

Daniel Gaida

Appl. Soft Comput., 2016

Efficient Global Optimization with Indefinite Kernels.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XIV, 2016

Tutorials at PPSN 2016.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XIV, 2016

2015

Comparison of different Methods for Univariate Time Series Imputation in R.

[BibT_eX]

[DOI]

Steffen Moritz

Alexis Sardá

CoRR, 2015

2014

Distance Measures for Permutations in Combinatorial Efficient Global Optimization.

[BibT_eX]

[DOI]

Proceedings of the Parallel Problem Solving from Nature - PPSN XIII, 2014

Efficient global optimization for combinatorial problems.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2014

Tuning multi-objective optimization algorithms for cyclone dust separators.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2014

2013

How to create meaningful and generalizable results.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2013

Comparing Ensemble-Based Forecasting Methods for Smart-Metering Data.

[BibT_eX]

[DOI]

Katya Vladislavleva

Proceedings of the Applications of Evolutionary Computation - 16th European Conference, 2013

A Case Study on Multi-Criteria Optimization of an Event Detection Software under Limited Budgets.

[BibT_eX]

[DOI]

Tobias Wagner

Michael Emmerich

Proceedings of the Evolutionary Multi-Criterion Optimization, 2013

2012

Multi-criteria optimization for hard problems under limited budgets.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2012

Statistical analysis of optimization algorithms with R.

[BibT_eX]

[DOI]

Mike Preuß

Proceedings of the Genetic and Evolutionary Computation Conference, 2012

Sequential parameter optimization for symbolic regression.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2012

SPOT applied to non-stochastic optimization problems: an experimental study.

[BibT_eX]

[DOI]

Proceedings of the Genetic and Evolutionary Computation Conference, 2012

2011

Noisy optimization with sequential parameter optimization and optimal computational budget allocation.

[BibT_eX]

[DOI]

Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, 2011

2010

Optimization of biogas production with computational intelligence a comparative study.

[BibT_eX]

[DOI]

Jörg Ziegenhirt

Wolfgang Konen