Martin Zaefferer | DeepAI

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Featured Co-authors

Christian Wolf
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∙ 01/25/2022

The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems

This paper reports on the first international competition on AI for the ...

3 Laurens Bliek, et al. ∙

∙ 07/29/2021

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

We have built a novel system for the surveillance of drinking water rese...

14 Jörg Stork, et al. ∙

∙ 07/19/2021

Experimental Investigation and Evaluation of Model-based Hyperparameter Optimization

Machine learning algorithms such as random forests or xgboost are gainin...

0 Eva Bartz, et al. ∙

∙ 05/17/2021

Behavior-based Neuroevolutionary Training in Reinforcement Learning

In addition to their undisputed success in solving classical optimizatio...

0 Jörg Stork, et al. ∙

∙ 05/16/2021

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

Crises like the COVID-19 pandemic pose a serious challenge to health-car...

0 Thomas Bartz-Beielstein, et al. ∙

∙ 09/03/2020

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...

0 Tom Peetz, et al. ∙

∙ 08/14/2020

Continuous Optimization Benchmarks by Simulation

Benchmark experiments are required to test, compare, tune, and understan...

0 Martin Zaefferer, et al. ∙

∙ 01/09/2020

Expected Improvement versus Predicted Value in Surrogate-Based Optimization

Surrogate-based optimization relies on so-called infill criteria (acquis...

12 Frederik Rehbach, et al. ∙

∙ 07/22/2019

Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning

In the last years, reinforcement learning received a lot of attention. O...

0 Jörg Stork, et al. ∙

∙ 07/16/2019

Prediction of neural network performance by phenotypic modeling

Surrogate models are used to reduce the burden of expensive-to-evaluate ...

0 Alexander Hagg, et al. ∙

∙ 02/09/2019

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

In NeuroEvolution, the topologies of artificial neural networks are opti...

0 Jörg Stork, et al. ∙

∙ 07/20/2018

Distance-based Kernels for Surrogate Model-based Neuroevolution

The topology optimization of artificial neural networks can be particula...

0 Jörg Stork, et al. ∙

∙ 07/10/2018

An Empirical Approach For Probing the Definiteness of Kernels

Models like support vector machines or Gaussian process regression often...

0 Martin Zaefferer, et al. ∙

∙ 07/03/2018

Linear Combination of Distance Measures for Surrogate Models in Genetic Programming

Surrogate models are a well established approach to reduce the number of...

0 Martin Zaefferer, et al. ∙

∙ 07/03/2018

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

Many real-world optimization problems require significant resources for ...

0 Martin Zaefferer, et al. ∙

∙ 12/12/2017

In a Nutshell: Sequential Parameter Optimization

The performance of optimization algorithms relies crucially on their par...

0 Thomas Bartz-Beielstein, et al. ∙

∙ 10/13/2015

Comparison of different Methods for Univariate Time Series Imputation in R

Missing values in datasets are a well-known problem and there are quite ...

0 Steffen Moritz, et al. ∙

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