123 citations · 419 across the 31 of their papers we have counts for
31 papers
Benchmarking that Matters: Rethinking Benchmarking for Practical Impact
Anna V. Kononova, Niki van Stein, Olaf Mersmann +14
Benchmarking has driven scientific progress in Evolutionary Computation, yet current practices fall short of real-world needs. Widely used synthetic suites such as BBOB and CEC iso…
A Novel Ranking Scheme for the Performance Analysis of Stochastic Optimization Algorithms using the Principles of Severity
Sowmya Chandrasekaran, Thomas Bartz-Beielstein
Stochastic optimization algorithms have been successfully applied in several domains to find optimal solutions. Because of the ever-growing complexity of the integrated systems, no…
Hyperparameter Tuning Cookbook: A guide for scikit-learn, PyTorch, river, and spotPython
Thomas Bartz-Beielstein
This document provides a comprehensive guide to hyperparameter tuning using spotPython for scikit-learn, PyTorch, and river. The first part introduces spotPython's surrogate model-…
PyTorch Hyperparameter Tuning - A Tutorial for spotPython
Thomas Bartz-Beielstein
The goal of hyperparameter tuning (or hyperparameter optimization) is to optimize the hyperparameters to improve the performance of the machine or deep learning model. spotPython (…
Underwater Acoustic Networks for Security Risk Assessment in Public Drinking Water Reservoirs
Jörg Stork, Philip Wenzel, Severin Landwein +8
We have built a novel system for the surveillance of drinking water reservoirs using underwater sensor networks. We implement an innovative AI-based approach to detect, classify an…
Experimental Investigation and Evaluation of Model-based Hyperparameter Optimization
Eva Bartz, Martin Zaefferer, Olaf Mersmann +1
Machine learning algorithms such as random forests or xgboost are gaining more importance and are increasingly incorporated into production processes in order to enable comprehensi…