Jörg Stork

574 citations
13 papers · 167 · h-index 5

Impact in

Papers in

Jörg Stork

13 papers receiving 156 citations

Peers

Jörg Stork
Comparison fields: 5 of 60
  • Computational Theory and Mathematics 61
  • Industrial and Manufacturing Engineering 28
  • Artificial Intelligence 77
  • Statistics, Probability and Uncertainty 16
  • Management Science and Operations Research 17
Replace Martin Zaefferer with:
Martin Zaefferer Germany
Ky Vu France
Henrik Linusson Sweden
Sandra M. Venske Brazil
Carlos Herrera Chile
Jiajie Mo China
Alexander Engau United States
Nirmala Sharma India
Boris Defourny United States
Jörg Stork relative to Martin Zaefferer Germany Martin Zaefferer's profile →
Citations per field
00.5×1.5×
Martin Zaefferer · 1×
Citations per year

Countries citing papers authored by Jörg Stork

Since Specialization
Citations

This map shows the geographic impact of Jörg Stork's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jörg Stork with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jörg Stork more than expected).

Fields of papers citing papers by Jörg Stork

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jörg Stork. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jörg Stork. The network helps show where Jörg Stork may publish in the future.

Co-authors

The 10 scholars most cited alongside Jörg Stork, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jörg Stork Line = papers co-authored together Jörg Stork links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1 202071
2 201435
3 201818
4 202116
5 202013
6 20144
7 20193
8
Modeling and Optimization of a Robust Gas Sensor
20162
9
Prediction of neural network performance by phenotypic modeling
20191
10 20171
11
Simulation and Optimization of Cyclone Dust Separators
20131
12
Surrogate-Assisted Learning of Neural Networks
20171
13 20171

About Jörg Stork

Jörg Stork is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research, Control and Systems Engineering and Computational Mechanics, having authored 13 papers that have together received 167 indexed citations. Recurring topics across this work include Advanced Multi-Objective Optimization Algorithms (6 papers), Evolutionary Algorithms and Applications (4 papers), Metaheuristic Optimization Algorithms Research (3 papers), Optimal Experimental Design Methods (3 papers), Cyclone Separators and Fluid Dynamics (2 papers), Gaussian Processes and Bayesian Inference (2 papers), Elevator Systems and Control (2 papers) and Aerosol Filtration and Electrostatic Precipitation (2 papers). The work is most often cited by research in Computational Theory and Mathematics (61 citations), Industrial and Manufacturing Engineering (28 citations), Artificial Intelligence (77 citations), Statistics, Probability and Uncertainty (16 citations) and Management Science and Operations Research (17 citations). Jörg Stork has collaborated with scholars based in Germany, Netherlands and France. Frequent co-authors include Thomas Bartz–Beielstein, A. E. Eiben, Martin Zaefferer, Andreas Fischbach, Boris Naujoks, Bogdan Filipič, Daniel Horn, Olaf Mersmann, Evert Haasdijk and Julien Hubert. Their work appears in journals such as Natural Computing, Applied Soft Computing, The International Journal of Advanced Manufacturing Technology, Publication Server of Bonn-Rhein-Sieg University of Applied Sciences (Bonn-Rhein-Sieg University of Applied Sciences) and Proceedings of the Genetic and Evolutionary Computation Conference Companion.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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