Martin Holeňa

2.3k total citations
103 papers, 1.6k citations indexed

About

Martin Holeňa is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Martin Holeňa has authored 103 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 55 papers in Artificial Intelligence, 31 papers in Computational Theory and Mathematics and 22 papers in Materials Chemistry. Recurrent topics in Martin Holeňa's work include Advanced Multi-Objective Optimization Algorithms (20 papers), Metaheuristic Optimization Algorithms Research (18 papers) and Evolutionary Algorithms and Applications (14 papers). Martin Holeňa is often cited by papers focused on Advanced Multi-Objective Optimization Algorithms (20 papers), Metaheuristic Optimization Algorithms Research (18 papers) and Evolutionary Algorithms and Applications (14 papers). Martin Holeňa collaborates with scholars based in Czechia, Germany and Canada. Martin Holeňa's co-authors include M. Baerns, Ulyana Zavyalova, Robert Schlögl, Uwe Rodemerck, Natasha Dropka, Evgenii V. Kondratenko, David Linke, D. Wolf, Quido Smejkal and Axel Barkschat and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Catalysis B: Environmental and ACS Catalysis.

In The Last Decade

Martin Holeňa

96 papers receiving 1.5k citations

Peers

Martin Holeňa
Jongsoo Park South Korea
Jon Paul Janet Sweden
Minglei Yang China
Dazhi Zhang China
Tanjin He United States
Vasilios I. Manousiouthakis United States
Kevin Maik Jablonka Switzerland
Robert Pollice Canada
Jian–wei Liu China
Jongsoo Park South Korea
Martin Holeňa
Citations per year, relative to Martin Holeňa Martin Holeňa (= 1×) peers Jongsoo Park

Countries citing papers authored by Martin Holeňa

Since Specialization
Citations

This map shows the geographic impact of Martin Holeňa'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 Martin Holeňa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Martin Holeňa more than expected).

Fields of papers citing papers by Martin Holeňa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Martin Holeňa. 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 Martin Holeňa. The network helps show where Martin Holeňa may publish in the future.

Co-authorship network of co-authors of Martin Holeňa

This figure shows the co-authorship network connecting the top 25 collaborators of Martin Holeňa. A scholar is included among the top collaborators of Martin Holeňa based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Martin Holeňa. Martin Holeňa is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Dropka, Natasha, et al.. (2025). Toward a Universal Czochralski Growth Model Leveraging Data‐Driven Techniques. Advanced Theory and Simulations. 8(12).
2.
Tang, Xia, et al.. (2023). Decision Tree-Supported Analysis of Gallium Arsenide Growth Using the LEC Method. Crystals. 13(12). 1659–1659. 5 indexed citations
3.
Holeňa, Martin, et al.. (2020). Two Semi-supervised Approaches to Malware Detection with Neural Networks.. 176–185. 2 indexed citations
4.
Holeňa, Martin, et al.. (2019). Rules Extraction from Neural Networks Trained on Multimedia Data.. 26–35.
5.
Holeňa, Martin, et al.. (2018). Automated Selection of Covariance Function for Gaussian process Surrogate Models.. 64–71. 2 indexed citations
6.
Holeňa, Martin, et al.. (2017). Adaptive Doubly Trained Evolution Control for the Covariance Matrix Adaptation Evolution Strategy.. ASEP. 120–128. 1 indexed citations
7.
Holeňa, Martin, et al.. (2017). Random-Forest-Based Analysis of URL Paths.. ASEP. 129–135. 2 indexed citations
8.
Holeňa, Martin, et al.. (2017). Breaking CAPTCHAs with Convolutional Neural Networks.. ASEP. 93–99. 9 indexed citations
9.
Holeňa, Martin, et al.. (2017). K-best Viterbi Semi-supervized Active Learning in Sequence Labelling.. ASEP. 144–152. 1 indexed citations
10.
Holeňa, Martin, et al.. (2016). Image Processing in Collaborative Open Narrative Systems.. ASEP. 155–162. 2 indexed citations
11.
Holeňa, Martin, et al.. (2016). Modeling and Clustering the Behavior of Animals Using Hidden Markov Models.. ASEP. 172–178. 1 indexed citations
12.
Holeňa, Martin, et al.. (2015). Evaluation of Association Rules Extracted during Anomaly Explanation. ASEP. 143–149. 1 indexed citations
13.
Holeňa, Martin, et al.. (2015). Comparing SVM, Gaussian Process and Random Forest Surrogate Models for the CMA-ES. ASEP. 186–193. 5 indexed citations
14.
Holeňa, Martin, et al.. (2015). Comparing Non-Linear Regression Methods on Black-Box Optimization Benchmarks.. ASEP. 135–142.
15.
Holeňa, Martin, et al.. (2012). Conformal sets in neural network regression.. ASEP. 17–24. 1 indexed citations
16.
Holeňa, Martin, et al.. (2012). Fuzzy classification rules based on similarity.. ASEP. 25–33. 1 indexed citations
17.
Holeňa, Martin, et al.. (2012). RBF-based surrogate model for evolutionary optimization.. ASEP. 3–8. 3 indexed citations
18.
Holeňa, Martin, et al.. (2011). Assessing the suitability of surrogate models in evolutionary optimization.. ASEP. 31–38. 2 indexed citations
19.
Holeňa, Martin, et al.. (2010). Dynamic classifier aggregation using fuzzy integral with interaction-sensitive fuzzy measure. ASEP. 3620. 225–230. 1 indexed citations
20.
Holeňa, Martin. (2006). Using neural networks to tune heuristic parameters in evolutionary optimization. International Conference on Artificial Intelligence. 1–6.

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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