1 code implementation • 18 Feb 2024 • Thomas Bartz-Beielstein
The `spotRiver` package provides a framework for hyperparameter tuning of OML models.
1 code implementation • 17 Jul 2023 • Thomas Bartz-Beielstein
This document provides a comprehensive guide to hyperparameter tuning using spotPython for scikit-learn, PyTorch, and river.
2 code implementations • 19 May 2023 • Thomas Bartz-Beielstein
In addition to an introduction to spotPython, this tutorial also includes a brief comparison with Ray Tune, a Python library for running experiments and tuning hyperparameters.
no code implementations • 29 Jul 2021 • Jörg Stork, Philip Wenzel, Severin Landwein, Maria-Elena Algorri, Martin Zaefferer, Wolfgang Kusch, Martin Staubach, Thomas Bartz-Beielstein, Hartmut Köhn, Hermann Dejager, Christian Wolf
We have built a novel system for the surveillance of drinking water reservoirs using underwater sensor networks.
no code implementations • 19 Jul 2021 • Eva Bartz, Martin Zaefferer, Olaf Mersmann, Thomas Bartz-Beielstein
The R package SPOT is used to perform the actual tuning (optimization).
1 code implementation • 12 Jul 2021 • Margarita Rebolledo, Daan Zeeuwe, Thomas Bartz-Beielstein, A. E. Eiben
In this paper, we mitigate this problem by extending our simulator with a battery model and taking energy consumption into account during fitness evaluations.
no code implementations • 30 May 2021 • Thomas Bartz-Beielstein, Frederik Rehbach, Amrita Sen, Martin Zaefferer
A surrogate model based hyperparameter tuning approach for deep learning is presented.
1 code implementation • 17 May 2021 • Jörg Stork, Martin Zaefferer, Nils Eisler, Patrick Tichelmann, Thomas Bartz-Beielstein, A. E. Eiben
In addition to their undisputed success in solving classical optimization problems, neuroevolutionary and population-based algorithms have become an alternative to standard reinforcement learning methods.
1 code implementation • 16 May 2021 • 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
Reasonable default values of these parameters were obtained in detailed discussions with medical professionals.
no code implementations • 25 Dec 2020 • Margarita Rebolledo, Sowmya Chandrasekaran, Thomas Bartz-Beielstein
In this report a non-exhaustive overview of optimization methods for flushing in WDS is given.
1 code implementation • 14 Dec 2020 • Thomas Bartz-Beielstein, Frederik Rehbach, Olaf Mersmann, Eva Bartz
There are benefits for medical professionals, e. g, analysis of the pandemic at local, regional, state and federal level, the consideration of special risk groups, tools for validating the length of stays and transition probabilities.
1 code implementation • 3 Dec 2020 • Jan Strohschein, Andreas Fischbach, Andreas Bunte, Heide Faeskorn-Woyke, Natalia Moriz, Thomas Bartz-Beielstein
The goal of this architecture is to reduce the implementation effort of artificial intelligence (AI) algorithms in CPPS.
no code implementations • 16 Nov 2020 • Sowmya Chandrasekaran, Margarita Rebolledo, Thomas Bartz-Beielstein
EventDetectR: An efficient Event Detection System (EDS) capable of detecting unexpected water quality conditions.
no code implementations • 3 Sep 2020 • Tom Peetz, Sebastian Vogt, Martin Zaefferer, Thomas Bartz-Beielstein
Generative Adversarial Networks (GANs) are powerful tools for generating new data for a variety of tasks.
no code implementations • 7 Jul 2020 • Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg, Jakob Bossek, Sowmya Chandrasekaran, Tome Eftimov, Andreas Fischbach, Pascal Kerschke, William La Cava, Manuel Lopez-Ibanez, Katherine M. Malan, Jason H. Moore, Boris Naujoks, Patryk Orzechowski, Vanessa Volz, Markus Wagner, Thomas Weise
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world.
no code implementations • 26 Feb 2020 • Andreas Fischbach, Jan Strohschein, Andreas Bunte, Jörg Stork, Heide Faeskorn-Woyke, Natalia Moriz, Thomas Bartz-Beielstein
The goal of the architecture is to reduce the implementation effort for the usage of artificial intelligence algorithms.
1 code implementation • 9 Jan 2020 • Frederik Rehbach, Martin Zaefferer, Boris Naujoks, Thomas Bartz-Beielstein
Few results from the literature show evidence, that under certain conditions, expected improvement may perform worse than something as simple as the predicted value of the surrogate model.
no code implementations • 18 Dec 2019 • Thomas Bartz-Beielstein
Some of these threats to society are well-known, e. g., weapons or killer robots.
no code implementations • 22 Jul 2019 • Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein, A. E. Eiben
In detail, we investigate a) the potential of SMB-NE with respect to evaluation efficiency and b) how to select adequate input sets for the phenotypic distance measure in a reinforcement learning problem.
no code implementations • 9 Feb 2019 • Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein
For these expensive optimization tasks, surrogate model-based optimization is frequently applied as it features a good evaluation efficiency.
no code implementations • 27 Aug 2018 • Jörg Stork, A. E. Eiben, Thomas Bartz-Beielstein
The extracted features of components or operators allow us to create a set of classification indicators to distinguish between a small number of classes.
no code implementations • 20 Jul 2018 • Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein
The topology optimization of artificial neural networks can be particularly difficult if the fitness evaluations require expensive experiments or simulations.
no code implementations • 10 Jul 2018 • Martin Zaefferer, Thomas Bartz-Beielstein, Günter Rudolph
We provide a proof-of-concept with 16 different distance measures for permutations.
no code implementations • 3 Jul 2018 • Martin Zaefferer, Jörg Stork, Oliver Flasch, Thomas Bartz-Beielstein
We investigate how different genotypic and phenotypic distance measures can be used to learn Kriging models as surrogates.
1 code implementation • 12 Dec 2017 • Thomas Bartz-Beielstein, Martin Zaefferer, Frederik Rehbach
The sequential parameter optimization (SPOT) package for R is a toolbox for tuning and understanding simulation and optimization algorithms.
1 code implementation • The R Journal 9(1) 2017 • Steffen Moritz, Thomas Bartz-Beielstein
The imputeTS package specializes on univariate time series imputation.