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Found 26 papers, 11 papers with code
Simplifying Hyperparameter Tuning in Online Machine Learning -- The spotRiverGUI
The `spotRiver` package provides a framework for hyperparameter tuning of OML models.
Hyperparameter Tuning Cookbook: A guide for scikit-learn, PyTorch, river, and spotPython
This document provides a comprehensive guide to hyperparameter tuning using spotPython for scikit-learn, PyTorch, and river.
PyTorch Hyperparameter Tuning - A Tutorial for spotPython
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.
Underwater Acoustic Networks for Security Risk Assessment in Public Drinking Water Reservoirs
We have built a novel system for the surveillance of drinking water reservoirs using underwater sensor networks.
Experimental Investigation and Evaluation of Model-based Hyperparameter Optimization
The R package SPOT is used to perform the actual tuning (optimization).
Impact of Energy Efficiency on the Morphology and Behaviour of Evolved Robots
In this paper, we mitigate this problem by extending our simulator with a battery model and taking energy consumption into account during fitness evaluations.
Surrogate Model Based Hyperparameter Tuning for Deep Learning with SPOT
A surrogate model based hyperparameter tuning approach for deep learning is presented.
Behavior-based Neuroevolutionary Training in Reinforcement Learning
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.
Resource Planning for Hospitals Under Special Consideration of the COVID-19 Pandemic: Optimization and Sensitivity Analysis
Reasonable default values of these parameters were obtained in detailed discussions with medical professionals.
Technical Report: Flushing Strategies in Drinking Water Systems
In this report a non-exhaustive overview of optimization methods for flushing in WDS is given.
Hospital Capacity Planning Using Discrete Event Simulation Under Special Consideration of the COVID-19 Pandemic
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.
Cognitive Capabilities for the CAAI in Cyber-Physical Production Systems
The goal of this architecture is to reduce the implementation effort of artificial intelligence (AI) algorithms in CPPS.
EventDetectR -- An Open-Source Event Detection System
EventDetectR: An efficient Event Detection System (EDS) capable of detecting unexpected water quality conditions.
Simulation of an Elevator Group Control Using Generative Adversarial Networks and Related AI Tools
Generative Adversarial Networks (GANs) are powerful tools for generating new data for a variety of tasks.
Benchmarking in Optimization: Best Practice and Open Issues
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world.
CAAI -- A Cognitive Architecture to Introduce Artificial Intelligence in Cyber-Physical Production Systems
The goal of the architecture is to reduce the implementation effort for the usage of artificial intelligence algorithms.
Expected Improvement versus Predicted Value in Surrogate-Based Optimization
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.
Why we need an AI-resilient society
Some of these threats to society are well-known, e. g., weapons or killer robots.
Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning
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.
Improving NeuroEvolution Efficiency by Surrogate Model-based Optimization with Phenotypic Distance Kernels
For these expensive optimization tasks, surrogate model-based optimization is frequently applied as it features a good evaluation efficiency.
A new Taxonomy of Continuous Global Optimization Algorithms
The extracted features of components or operators allow us to create a set of classification indicators to distinguish between a small number of classes.
Distance-based Kernels for Surrogate Model-based Neuroevolution
The topology optimization of artificial neural networks can be particularly difficult if the fitness evaluations require expensive experiments or simulations.
An Empirical Approach For Probing the Definiteness of Kernels
We provide a proof-of-concept with 16 different distance measures for permutations.
Linear Combination of Distance Measures for Surrogate Models in Genetic Programming
We investigate how different genotypic and phenotypic distance measures can be used to learn Kriging models as surrogates.
In a Nutshell -- The Sequential Parameter Optimization Toolbox
The sequential parameter optimization (SPOT) package for R is a toolbox for tuning and understanding simulation and optimization algorithms.
imputeTS: Time Series Missing Value Imputation in R
The imputeTS package specializes on univariate time series imputation.
