86 citations · 136 across the 21 of their papers we have counts for
21 papers
Variational Quantum Conditional Boltzmann Machines for Time-Series Forecasting: Architectures, Symmetric Hyperparameter Evaluation, and a Nonlinear Benchmark
Gerhard Hellstern, Danyal Maheshwari, Martin Zaefferer +2
In this study, we developed and evaluated four conditional energy-based forecasting architectures: a classical Gaussian-Bernoulli CRBM, a hybrid quantum-classical QCRBM, a full-reg…
Learning Temporal Patterns in Financial Time Series: A Comparative Study of Quantum LSTM and Quantum Reservoir Computing
Danyal Maheshwari, Gerhard Hellstern, Martin Zaefferer +2
This study explores quantum and classical hybrid architectures for financial time-series fore casting, focusing on Quantum Long Short-Term Memory (QLSTM) networks and Quantum Reser…
Extrapolation method to optimize linear-ramp QAOA parameters: Evaluation of QAOA runtime scaling
Vanessa Dehn, Martin Zaefferer, Gerhard Hellstern +3
The Quantum Approximate Optimization Algorithm (QAOA) has been suggested as a promising candidate for the solution of combinatorial optimization problems. Yet, whether - or under w…
Quantum computer based Feature Selection in Machine Learning
Gerhard Hellstern, Vanessa Dehn, Martin Zaefferer
The problem of selecting an appropriate number of features in supervised learning problems is investigated in this paper. Starting with common methods in machine learning, we treat…
The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems
Laurens Bliek, Paulo da Costa, Reza Refaei Afshar +19
This paper reports on the first international competition on AI for the traveling salesman problem (TSP) at the International Joint Conference on Artificial Intelligence 2021 (IJCA…
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…
Behavior-based Neuroevolutionary Training in Reinforcement Learning
Jörg Stork, Martin Zaefferer, Nils Eisler +3
In addition to their undisputed success in solving classical optimization problems, neuroevolutionary and population-based algorithms have become an alternative to standard reinfor…
Resource Planning for Hospitals Under Special Consideration of the COVID-19 Pandemic: Optimization and Sensitivity Analysis
Thomas Bartz-Beielstein, Marcel Dröscher, Alpar Gür +9
Crises like the COVID-19 pandemic pose a serious challenge to health-care institutions. They need to plan the resources required for handling the increased load, for instance, hosp…
Simulation of an Elevator Group Control Using Generative Adversarial Networks and Related AI Tools
Tom Peetz, Sebastian Vogt, Martin Zaefferer +1
Testing new, innovative technologies is a crucial task for safety and acceptance. But how can new systems be tested if no historical real-world data exist? Simulation provides an a…
Continuous Optimization Benchmarks by Simulation
Martin Zaefferer, Frederik Rehbach
Benchmark experiments are required to test, compare, tune, and understand optimization algorithms. Ideally, benchmark problems closely reflect real-world problem behavior. Yet, rea…
Expected Improvement versus Predicted Value in Surrogate-Based Optimization
Frederik Rehbach, Martin Zaefferer, Boris Naujoks +1
Surrogate-based optimization relies on so-called infill criteria (acquisition functions) to decide which point to evaluate next. When Kriging is used as the surrogate model of choi…
Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning
Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein +1
In the last years, reinforcement learning received a lot of attention. One method to solve reinforcement learning tasks is Neuroevolution, where neural networks are optimized by ev…
Prediction of neural network performance by phenotypic modeling
Alexander Hagg, Martin Zaefferer, Jörg Stork +1
Surrogate models are used to reduce the burden of expensive-to-evaluate objective functions in optimization. By creating models which map genomes to objective values, these models…
Improving NeuroEvolution Efficiency by Surrogate Model-based Optimization with Phenotypic Distance Kernels
Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein
In NeuroEvolution, the topologies of artificial neural networks are optimized with evolutionary algorithms to solve tasks in data regression, data classification, or reinforcement…
Distance-based Kernels for Surrogate Model-based Neuroevolution
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. For that reason, the…
An Empirical Approach For Probing the Definiteness of Kernels
Martin Zaefferer, Thomas Bartz-Beielstein, Günter Rudolph
Models like support vector machines or Gaussian process regression often require positive semi-definite kernels. These kernels may be based on distance functions. While definitenes…
Linear Combination of Distance Measures for Surrogate Models in Genetic Programming
Martin Zaefferer, Jörg Stork, Oliver Flasch +1
Surrogate models are a well established approach to reduce the number of expensive function evaluations in continuous optimization. In the context of genetic programming, surrogate…
A First Analysis of Kernels for Kriging-based Optimization in Hierarchical Search Spaces
Martin Zaefferer, Daniel Horn
Many real-world optimization problems require significant resources for objective function evaluations. This is a challenge to evolutionary algorithms, as it limits the number of a…
In a Nutshell -- The Sequential Parameter Optimization Toolbox
Thomas Bartz-Beielstein, Martin Zaefferer, Frederik Rehbach
The performance of optimization algorithms relies crucially on their parameterizations. Finding good parameter settings is called algorithm tuning. The sequential parameter optimiz…
Comparison of different Methods for Univariate Time Series Imputation in R
Steffen Moritz, Alexis Sardá, Thomas Bartz-Beielstein +2
Missing values in datasets are a well-known problem and there are quite a lot of R packages offering imputation functions. But while imputation in general is well covered within R,…