Thomas Bartz-Beielstein | DeepAI

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∙ 07/17/2023

Hyperparameter Tuning Cookbook: A guide for scikit-learn, PyTorch, river, and spotPython

This document provides a comprehensive guide to hyperparameter tuning us...

0 Thomas Bartz-Beielstein, et al. ∙

∙ 05/19/2023

PyTorch Hyperparameter Tuning - A Tutorial for spotPython

The goal of hyperparameter tuning (or hyperparameter optimization) is to...

23 Thomas Bartz-Beielstein, 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. ∙

∙ 07/12/2021

Impact of Energy Efficiency on the Morphology and Behaviour of Evolved Robots

Most evolutionary robotics studies focus on evolving some targeted behav...

1 Margarita Rebolledo, et al. ∙

∙ 05/30/2021

Surrogate Model Based Hyperparameter Tuning for Deep Learning with SPOT

A surrogate model based hyperparameter tuning approach for deep learning...

0 Thomas Bartz-Beielstein, 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. ∙

∙ 12/25/2020

Technical Report: Flushing Strategies in Drinking Water Systems

Drinking water supply and distribution systems are critical infrastructu...

0 Margarita Rebolledo, et al. ∙

∙ 12/14/2020

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...

27 Thomas Bartz-Beielstein, et al. ∙

∙ 12/03/2020

Cognitive Capabilities for the CAAI in Cyber-Physical Production Systems

This paper presents the cognitive module of the cognitive architecture f...

0 Jan Strohschein, et al. ∙

∙ 11/16/2020

EventDetectR – An Open-Source Event Detection System

EventDetectR: An efficient Event Detection System (EDS) capable of detec...

0 Sowmya Chandrasekaran, et al. ∙

∙ 09/06/2020

Optimization of High-dimensional Simulation Models Using Synthetic Data

Simulation models are valuable tools for resource usage estimation and c...

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. ∙

∙ 07/07/2020

Benchmarking in Optimization: Best Practice and Open Issues

This survey compiles ideas and recommendations from more than a dozen re...

11 Thomas Bartz-Beielstein, et al. ∙

∙ 02/26/2020

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

This paper introduces CAAI, a novel cognitive architecture for artificia...

0 Andreas Fischbach, 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. ∙

∙ 12/18/2019

Why we need an AI-resilient society

Artificial intelligence is considered as a key technology. It has a huge...

0 Thomas Bartz-Beielstein, 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. ∙

∙ 02/22/2019

Evaluation of Cognitive Architectures for Cyber-Physical Production Systems

Cyber-physical production systems (CPPS) integrate physical and computat...

0 Andreas Bunte, 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. ∙

∙ 08/27/2018

A new Taxonomy of Continuous Global Optimization Algorithms

Surrogate-based optimization and nature-inspired metaheuristics have bec...

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. ∙

∙ 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. ∙

∙ 06/23/2010

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 ...

0 Thomas Bartz-Beielstein, et al. ∙

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