Thomas Bartz-Beielstein

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Technical Report: Flushing Strategies in Drinking Water Systems

Drinking water supply and distribution systems are critical infrastructu...
12/25/2020 ∙ by Margarita Rebolledo, et al. ∙ 0 ∙ share

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Hospital Capacity Planning Using Discrete Event Simulation Under Special Consideration of the COVID-19 Pandemic

We present a resource-planning tool for hospitals under special consider...
12/14/2020 ∙ by Thomas Bartz-Beielstein, et al. ∙ 27 ∙ share

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Cognitive Capabilities for the CAAI in Cyber-Physical Production Systems

This paper presents the cognitive module of the cognitive architecture f...
12/03/2020 ∙ by Jan Strohschein, et al. ∙ 0 ∙ share

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EventDetectR – An Open-Source Event Detection System

EventDetectR: An efficient Event Detection System (EDS) capable of detec...
11/16/2020 ∙ by Sowmya Chandrasekaran, et al. ∙ 0 ∙ share

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Optimization of High-dimensional Simulation Models Using Synthetic Data

Simulation models are valuable tools for resource usage estimation and c...
09/06/2020 ∙ by Thomas Bartz-Beielstein, et al. ∙ 0 ∙ share

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

Testing new, innovative technologies is a crucial task for safety and ac...
09/03/2020 ∙ by Tom Peetz, et al. ∙ 0 ∙ share

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Benchmarking in Optimization: Best Practice and Open Issues

This survey compiles ideas and recommendations from more than a dozen re...
07/07/2020 ∙ by Thomas Bartz-Beielstein, et al. ∙ 11 ∙ share

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CAAI – A Cognitive Architecture to Introduce Artificial Intelligence in Cyber-Physical Production Systems

This paper introduces CAAI, a novel cognitive architecture for artificia...
02/26/2020 ∙ by Andreas Fischbach, et al. ∙ 0 ∙ share

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Expected Improvement versus Predicted Value in Surrogate-Based Optimization

Surrogate-based optimization relies on so-called infill criteria (acquis...
01/09/2020 ∙ by Frederik Rehbach, et al. ∙ 12 ∙ share

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Why we need an AI-resilient society

Artificial intelligence is considered as a key technology. It has a huge...
12/18/2019 ∙ by Thomas Bartz-Beielstein, et al. ∙ 0 ∙ share

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Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning

In the last years, reinforcement learning received a lot of attention. O...
07/22/2019 ∙ by Jörg Stork, et al. ∙ 0 ∙ share

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Evaluation of Cognitive Architectures for Cyber-Physical Production Systems

Cyber-physical production systems (CPPS) integrate physical and computat...
02/22/2019 ∙ by Andreas Bunte, et al. ∙ 0 ∙ share

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

In NeuroEvolution, the topologies of artificial neural networks are opti...
02/09/2019 ∙ by Jörg Stork, et al. ∙ 0 ∙ share

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A new Taxonomy of Continuous Global Optimization Algorithms

Surrogate-based optimization and nature-inspired metaheuristics have bec...
08/27/2018 ∙ by Jörg Stork, et al. ∙ 0 ∙ share

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Distance-based Kernels for Surrogate Model-based Neuroevolution

The topology optimization of artificial neural networks can be particula...
07/20/2018 ∙ by Jörg Stork, et al. ∙ 0 ∙ share

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An Empirical Approach For Probing the Definiteness of Kernels

Models like support vector machines or Gaussian process regression often...
07/10/2018 ∙ by Martin Zaefferer, et al. ∙ 0 ∙ share

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Linear Combination of Distance Measures for Surrogate Models in Genetic Programming

Surrogate models are a well established approach to reduce the number of...
07/03/2018 ∙ by Martin Zaefferer, et al. ∙ 0 ∙ share

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In a Nutshell: Sequential Parameter Optimization

The performance of optimization algorithms relies crucially on their par...
12/12/2017 ∙ by Thomas Bartz-Beielstein, et al. ∙ 0 ∙ share

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Comparison of different Methods for Univariate Time Series Imputation in R

Missing values in datasets are a well-known problem and there are quite ...
10/13/2015 ∙ by Steffen Moritz, et al. ∙ 0 ∙ share

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SPOT: An R Package For Automatic and Interactive Tuning of Optimization Algorithms by Sequential Parameter Optimization

The sequential parameter optimization (SPOT) package for R is a toolbox ...
06/23/2010 ∙ by Thomas Bartz-Beielstein, et al. ∙ 0 ∙ share

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Thomas Bartz-Beielstein

Featured Co-authors

Carola Doerr

Markus Wagner

Jason H. Moore

Pascal Kerschke

Jakob Bossek

Vanessa Volz

Martin Zaefferer

A. E. Eiben

Jörg Stork

Boris Naujoks

Patryk Orzechowski

Technical Report: Flushing Strategies in Drinking Water Systems

Hospital Capacity Planning Using Discrete Event Simulation Under Special Consideration of the COVID-19 Pandemic

Cognitive Capabilities for the CAAI in Cyber-Physical Production Systems

EventDetectR – An Open-Source Event Detection System

Optimization of High-dimensional Simulation Models Using Synthetic Data

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

Benchmarking in Optimization: Best Practice and Open Issues

CAAI – A Cognitive Architecture to Introduce Artificial Intelligence in Cyber-Physical Production Systems

Expected Improvement versus Predicted Value in Surrogate-Based Optimization

Why we need an AI-resilient society

Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning

Evaluation of Cognitive Architectures for Cyber-Physical Production Systems

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

A new Taxonomy of Continuous Global Optimization Algorithms

Distance-based Kernels for Surrogate Model-based Neuroevolution

An Empirical Approach For Probing the Definiteness of Kernels

Linear Combination of Distance Measures for Surrogate Models in Genetic Programming

In a Nutshell: Sequential Parameter Optimization

Comparison of different Methods for Univariate Time Series Imputation in R

SPOT: An R Package For Automatic and Interactive Tuning of Optimization Algorithms by Sequential Parameter Optimization