Framework for Distributed Evolutionary Algorithms in Computational Grids. - Steffen Limmer; Dietmar Fey (ISICA (1), 2010)

Simulation and Optimized Design of High Density Optical Crossconnect Systems for Massively Parallel Computing Architectures. - Ulrich Lohmann; Jürgen Jahns; Steffen Limmer; Dietmar Fey (OSC, 2010)

Unconventional Computing - Reversible Signed Digit Adders for Future Nanocomputing Devices (Unkonventionelles Rechnen - reversible Addierer für künftiges Nanocomputing). - Dietmar Fey; Steffen Limmer (it Inf. Technol., 2010)

Evolutionary optimization of layouts for high density free space optical network links. - Steffen Limmer; Dietmar Fey; Ulrich Lohmann; Jürgen Jahns (GECCO, 2011)

Evolutionary Design of Active Free Space Optical Networks Based on Digital Mirror Devices. - Steffen Limmer; Dietmar Fey; Ulrich Lohmann; Jürgen Jahns (EvoApplications, 2012)

Exploiting interference for efficient distributed computation in cluster-based wireless sensor networks. - Steffen Limmer; Slawomir Stanczak; Mario Goldenbaum; Renato L. G. Cavalcante (GlobalSIP, 2013)

Performance investigations of genetic algorithms on graphics cards. - Johannes Hofmann 001; Steffen Limmer; Dietmar Fey (Swarm Evol. Comput., 2013)

Performance Investigation and Tuning in the Interoperable Cloud4E Platform. - Steffen Limmer; Maik Srba; Dietmar Fey (Euro-Par Workshops (2), 2014)

On ℓp-norm computation over multiple-access channels. - Steffen Limmer; Slawomir Stanczak (ITW, 2014)

On ℓp-norm Computation over Multiple-Access Channels. - Steffen Limmer; Slawomir Stanczak (CoRR, 2014)

Services for numerical simulations and optimisations in grids. - Steffen Limmer; André Schneider; Christian Boehme; Dietmar Fey; Simon Schmitz; Achim Graupner; Martin Sülzle (Int. J. Parallel Emergent Distributed Syst., 2014)

A simple algorithm for approximation by nomographic functions. - Steffen Limmer; Jafar Mohammadi; Slawomir Stanczak (Allerton, 2015)

Vergleich gebräuchlicher Parallelrechensysteme für die Beschleunigung evolutionärer Algorithmen demonstriert für den Entwurf miniaturisierter optischer und elektronischer Bauelemente. - Steffen Limmer (2015)

Investigation of strategies for an increasing population size in multi-objective CMA-ES. - Steffen Limmer; Dietmar Fey (CEC, 2016)

Towards optimal nonlinearities for sparse recovery using higher-order statistics. - Steffen Limmer; Slawomir Stanczak (MLSP, 2016)

Multi-objective optimization of plug-in electric vehicle charging prices. - Steffen Limmer; Tobias Rodemann (SSCI, 2017)

Comparison of common parallel architectures for the execution of the island model and the global parallelization of evolutionary algorithms. - Steffen Limmer; Dietmar Fey (Concurr. Comput. Pract. Exp., 2017)

Optimal deep neural networks for sparse recovery via Laplace techniques. - Steffen Limmer; Slawomir Stanczak (CoRR, 2017)

Distributed Machine Learning in the Context of Function Computation over Wireless Networks. - Miruna Raceala-Motoc; Steffen Limmer; Igor Bjelakovic; Slawomir Stanczak (ACSSC, 2018)

Robust Evolutionary Optimization Based on Coevolution. - Steffen Limmer; Tobias Rodemann (EvoApplications, 2018)

Optimization of Dynamic Prices for Electric Vehicle Charging Considering Fairness. - Steffen Limmer; Manuel Dietrich (SSCI, 2018)

A Neural Architecture for Bayesian Compressive Sensing Over the Simplex via Laplace Techniques. - Steffen Limmer; Slawomir Stanczak (IEEE Trans. Signal Process., 2018)

Vehicle Fleet Maintenance Scheduling Optimization by Multi-objective Evolutionary Algorithms. - Yali Wang 002; Steffen Limmer; Markus Olhofer; Michael T. M. Emmerich; Thomas Bäck (CEC, 2019)

A tabu search-based memetic algorithm for the multi-objective flexible job shop scheduling problem. - Marios Kefalas; Steffen Limmer; Asteris Apostolidis; Markus Olhofer; Michael Emmerich 001; Thomas Bäck (GECCO (Companion), 2019)

Automated Machine Learning for Short-term Electric Load Forecasting. - Can Wang; Thomas Bäck; Holger H. Hoos; Mitra Baratchi; Steffen Limmer; Markus Olhofer (SSCI, 2019)

Optimizing the Hyperparameters of a Mixed Integer Linear Programming Solver to Speed up Electric Vehicle Charging Control. - Takahiro Ishihara; Steffen Limmer (EvoApplications, 2020)

Interpretable Control by Reinforcement Learning. - Daniel Hein 001; Steffen Limmer; Thomas A. Runkler (CoRR, 2020)

A Combination of Fourier Transform and Machine Learning for Fault Detection and Diagnosis of Induction Motors. - Van Duc Nguyen; Ewout Zwanenburg; Steffen Limmer; Wessel Luijben; Thomas Bäck; Markus Olhofer (DSA, 2021)

Coordination of Home Appliances for Demand Response: An Improved Optimization Model and Approach. - Steffen Limmer; Fernando Lezama; João P. Soares; Zita A. Vale (IEEE Access, 2021)

Automatic preference based multi-objective evolutionary algorithm on vehicle fleet maintenance scheduling optimization. - Yali Wang 002; Steffen Limmer; Markus Olhofer; Michael Emmerich 001; Thomas Bäck (Swarm Evol. Comput., 2021)

Investigating the Use of Linear Programming and Evolutionary Algorithms for Multi-Objective Electric Vehicle Charging Problem. - Hemant Kumar Singh; Tapabrata Ray; Md Juel Rana; Steffen Limmer; Tobias Rodemann; Markus Olhofer (IEEE Access, 2022)

Computational Methods for Scheduling the Charging and Assignment of an On-Site Shared Electric Vehicle Fleet. - Johannes Varga; Günther R. Raidl; Steffen Limmer (IEEE Access, 2022)

Comparison and Evaluation of Methods for a Predict+Optimize Problem in Renewable Energy. - Christoph Bergmeir; Frits de Nijs; Abishek Sriramulu; Mahdi Abolghasemi; Richard Bean 001; John M. Betts; Quang Bui; Nam Trong Dinh; Nils Einecke; Rasul Esmaeilbeigi; Scott Ferraro; Priya Galketiya; et al. (CoRR, 2022)

An Evolutionary Approach for Scheduling a Fleet of Shared Electric Vehicles. - Steffen Limmer; Johannes Varga; Günther R. Raidl (EvoApplications@EvoStar, 2023)

Hybridizing TPOT with Bayesian Optimization. - Angus Kenny; Tapabrata Ray; Steffen Limmer; Hemant Kumar Singh; Tobias Rodemann; Markus Olhofer (GECCO, 2023)

Physics-Informed Neural Networks for Pathloss Prediction. - Steffen Limmer; Alberto Martínez Alba; Nicola Michailow (MLSP, 2023)

A Hybrid Quantum-Classical Approach to the Electric Mobility Problem. - Margarita Veshchezerova; Mikhail Somov; David Bertsche; Steffen Limmer; Sebastian Schmitt; Michael Perelshtein; Ayush Joshi Tripathi (QCE, 2023)

The Energy Prediction Smart-Meter Dataset: Analysis of Previous Competitions and Beyond. - Direnc Pekaslan; Jose Maria Alonso-Moral; Kasun Bandara; Christoph Bergmeir; Juan Bernabé-Moreno; Robert Eigenmann; Nils Einecke; Selvi Ergen; Rakshitha Godahewa; Hansika Hewamalage; Jesus Lago; Steffen Limmer; et al. (CoRR, 2023)

Improvements in Large Neighborhood Search for the Electric Autonomous Dial-A-Ride Problem. - Maria Bresich; Günther R. Raidl; Steffen Limmer (EUROCAST (1), 2024)

A Hierarchical Dissimilarity Metric for Automated Machine Learning Pipelines, and Visualizing Search Behaviour. - Angus Kenny; Tapabrata Ray; Steffen Limmer; Hemant Kumar Singh; Tobias Rodemann; Markus Olhofer (EvoApplications@EvoStar, 2024)

Letting a Large Neighborhood Search for an Electric Dial-A-Ride Problem Fly: On-The-Fly Charging Station Insertion. - Maria Bresich; Günther R. Raidl; Steffen Limmer (GECCO, 2024)

Using Bayesian Optimization to Improve Hyperparameter Search in TPOT. - Angus Kenny; Tapabrata Ray; Steffen Limmer; Hemant Kumar Singh; Tobias Rodemann; Markus Olhofer (GECCO, 2024)

Neural-ANOVA: Model Decomposition for Interpretable Machine Learning. - Steffen Limmer; Steffen Udluft; Clemens Otte (CoRR, 2024)

Automated machine learning: past, present and future. - Mitra Baratchi; Can Wang; Steffen Limmer; Jan N. van Rijn; Holger H. Hoos; Thomas Bäck; Markus Olhofer (Artif. Intell. Rev., 2024)

Genetic Programming Hyper-Heuristic for the Dynamic Electric Dial-a-Ride Problem. - William Huang; Yi Mei 001; Günther R. Raidl; Fangfang Zhang 003; Laurenz Tomandl; Steffen Limmer; Mengjie Zhang 001; Tobias Rodemann (CEC, 2025)

Predict+Optimize Problem in Renewable Energy Scheduling. - Christoph Bergmeir; Frits de Nijs; Evgenii Genov; Abishek Sriramulu; Mahdi Abolghasemi; Richard Bean 001; John M. Betts; Quang Bui; Nam Trong Dinh; Nils Einecke; Rasul Esmaeilbeigi; Scott Ferraro; et al. (IEEE Access, 2025)

A systematic review of explainability in computational intelligence for optimization. - José Almeida 002; João P. Soares; Fernando Lezama; Steffen Limmer; Tobias Rodemann; Zita Vale (Comput. Sci. Rev., 2025)

A Real-World Energy Management Dataset from a Smart Company Building for Optimization and Machine Learning. - Jens Engel; Andrea Castellani; Patricia Wollstadt; Felix Lanfermann; Thomas Schmitt; Sebastian Schmitt; Lydia Fischer; Steffen Limmer; David Luttropp; Florian Jomrich; René Unger; Tobias Rodemann (CoRR, 2025)