André Deutz

2.3k total citations · 1 hit paper
31 papers, 1.1k citations indexed

About

André Deutz is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, André Deutz has authored 31 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Computational Theory and Mathematics, 16 papers in Artificial Intelligence and 8 papers in Management Science and Operations Research. Recurrent topics in André Deutz's work include Advanced Multi-Objective Optimization Algorithms (22 papers), Metaheuristic Optimization Algorithms Research (13 papers) and Evolutionary Algorithms and Applications (8 papers). André Deutz is often cited by papers focused on Advanced Multi-Objective Optimization Algorithms (22 papers), Metaheuristic Optimization Algorithms Research (13 papers) and Evolutionary Algorithms and Applications (8 papers). André Deutz collaborates with scholars based in Netherlands, Germany and Mexico. André Deutz's co-authors include Michael Emmerich, Thomas Bäck, Kaifeng Yang, Hao Wang, Renate Schneider, Iryna Yevseyeva, Eric R. Lacy, K. Janitschke, Bernd Gollackner and Julia Walochnik and has published in prestigious journals such as Gastroenterology, Information Sciences and IEEE Transactions on Cybernetics.

In The Last Decade

André Deutz

30 papers receiving 1.0k citations

Hit Papers

A tutorial on multiobjective optimization: fundamentals a... 2018 2026 2020 2023 2018 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. A tutorial on multiobjective optimization: fundamentals and evolutionary methods
    2018 429 citations Michael Emmerich, André Deutz Natural Computing profile →

Peers

André Deutz
Jianyong Sun China
Masahiro Tanaka Japan
J. Fraser Forbes Canada
K.-T. Fang
A. Ismael F. Vaz Portugal
Sébastien Le Digabel Canada
Andrea Grosso Italy
Qin Zhang China
Qinghua Su China
A. L. Custódio Portugal
Jianyong Sun China
André Deutz
Citations per year, relative to André Deutz André Deutz (= 1×) peers Jianyong Sun

Countries citing papers authored by André Deutz

Since Specialization
Citations

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

Fields of papers citing papers by André Deutz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of André Deutz

This figure shows the co-authorship network connecting the top 25 collaborators of André Deutz. A scholar is included among the top collaborators of André Deutz 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 André Deutz. André Deutz 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.
Wang, Hao, et al.. (2024). A Newton Method for Hausdorff Approximations of the Pareto Front Within Multiobjective Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation. 29(5). 2104–2115. 5 indexed citations
2.
Wang, Hao, et al.. (2023). The Hypervolume Newton Method for Constrained Multi-Objective Optimization Problems. Mathematical and Computational Applications. 28(1). 10–10. 1 indexed citations
3.
Bäck, Thomas, et al.. (2020). Parallel Problem Solving from Nature – PPSN XVI. Lecture notes in computer science. 35 indexed citations
4.
Grimme, Christian, Pascal Kerschke, Michael Emmerich, et al.. (2019). Sliding to the global optimum: How to benefit from non-global optima in multimodal multi-objective optimization. AIP conference proceedings. 2070. 20052–20052. 5 indexed citations
5.
Yang, Kaifeng, Michael Emmerich, André Deutz, & Thomas Bäck. (2019). Efficient computation of expected hypervolume improvement using box decomposition algorithms. Journal of Global Optimization. 75(1). 3–34. 58 indexed citations
6.
Emmerich, Michael & André Deutz. (2018). A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Natural Computing. 17(3). 585–609. 429 indexed citations breakdown →
7.
Yang, Kaifeng, Michael Emmerich, André Deutz, & Thomas Bäck. (2018). Multi-Objective Bayesian Global Optimization using expected hypervolume improvement gradient. Swarm and Evolutionary Computation. 44. 945–956. 95 indexed citations
8.
Schütze, Oliver, et al.. (2018). The Set-Based Hypervolume Newton Method for Bi-Objective Optimization. IEEE Transactions on Cybernetics. 50(5). 2186–2196. 31 indexed citations
9.
Emmerich, Michael, et al.. (2017). EVOLVE – A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation VII. Studies in computational intelligence. 5 indexed citations
10.
Kerschke, Pascal, Hao Wang, Mike Preuß, et al.. (2017). Towards analyzing multimodality of multiobjective landscapes. 9(3). 14–14. 6 indexed citations
11.
Yang, Kaifeng, Longmei Li, André Deutz, Thomas Bäck, & Michael Emmerich. (2016). Preference-based multiobjective optimization using truncated expected hypervolume improvement. 276–281. 14 indexed citations
12.
Emmerich, Michael, et al.. (2016). Maximizing Consensus in Portfolio Selection in Multicriteria Group Decision Making. Procedia Computer Science. 100. 848–855. 1 indexed citations
13.
Emmerich, Michael, André Deutz, & Iryna Yevseyeva. (2015). A Bayesian Approach to Portfolio Selection in Multicriteria Group Decision Making. Procedia Computer Science. 64. 993–1000. 7 indexed citations
14.
Emmerich, Michael, André Deutz, Thomas Bäck, et al.. (2013). EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV. Advances in intelligent systems and computing. 9 indexed citations
15.
Emmerich, Michael, André Deutz, Thomas Bäck, et al.. (2013). EVOLVE - A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation IV [EVOLVE 2013, Leiden The Netherlands, July 10-13, 2013]. 1 indexed citations
16.
Kruisselbrink, Johannes W., et al.. (2011). Using the uncertainty handling CMA-ES for finding robust optima. 877–884. 5 indexed citations
17.
Obwaller, Andreas, Renate Schneider, Julia Walochnik, et al.. (2004). Echinococcus granulosusstrain differentiation based on sequence heterogeneity in mitochondrial genes of cytochromecoxidase-1 and NADH dehydrogenase-1. Parasitology. 128(5). 569–575. 72 indexed citations
18.
Deutz, André, Andrzej Ehrenfeucht, & Grzegorz Rozenberg. (1994). Hyperedge channels are abelian. Theoretical Computer Science. 127(2). 387–393.
19.
Deutz, André, Andrzej Ehrenfeucht, & Grzegorz Rozenberg. (1994). Clans and regions in 2-structures. Theoretical Computer Science. 129(2). 207–262. 2 indexed citations
20.
Lacy, Eric R., et al.. (1991). A rapid, accurate, immunohistochemical method to label proliferating cells in the digestive tract. Gastroenterology. 100(1). 259–262. 54 indexed citations

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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