Tome Eftimov

2.4k total citations
124 papers, 1.2k citations indexed

About

Tome Eftimov is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Tome Eftimov has authored 124 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 80 papers in Artificial Intelligence, 40 papers in Computational Theory and Mathematics and 35 papers in Molecular Biology. Recurrent topics in Tome Eftimov's work include Advanced Multi-Objective Optimization Algorithms (36 papers), Metaheuristic Optimization Algorithms Research (34 papers) and Biomedical Text Mining and Ontologies (25 papers). Tome Eftimov is often cited by papers focused on Advanced Multi-Objective Optimization Algorithms (36 papers), Metaheuristic Optimization Algorithms Research (34 papers) and Biomedical Text Mining and Ontologies (25 papers). Tome Eftimov collaborates with scholars based in Slovenia, North Macedonia and France. Tome Eftimov's co-authors include Barbara Koroušić Seljak, Peter Korošec, Gorjan Popovski, Dragi Kocev, Matej Petković, Monika Simjanoska, Tamara Bucher, Nives Ogrinc, Carola Doerr and Doris Potočnik and has published in prestigious journals such as PLoS ONE, Scientific Reports and Food Chemistry.

In The Last Decade

Tome Eftimov

112 papers receiving 1.2k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Tome Eftimov Slovenia 18 622 269 245 182 146 124 1.2k
Barbara Koroušić Seljak Slovenia 21 373 0.6× 278 1.0× 59 0.2× 593 3.3× 202 1.4× 97 1.5k
José Camacho Spain 25 394 0.6× 235 0.9× 42 0.2× 34 0.2× 88 0.6× 91 1.9k
James M. Davenport United States 18 499 0.8× 109 0.4× 114 0.5× 33 0.2× 17 0.1× 43 1.8k
Linda C. van der Gaag Netherlands 19 821 1.3× 82 0.3× 124 0.5× 13 0.1× 41 0.3× 95 1.3k
Dragan Gamberger Croatia 17 686 1.1× 134 0.5× 173 0.7× 26 0.1× 11 0.1× 60 1.2k
Xiaofang Chen China 22 504 0.8× 31 0.1× 152 0.6× 40 0.2× 11 0.1× 158 1.6k
Anil Aswani United States 16 171 0.3× 213 0.8× 57 0.2× 78 0.4× 6 0.0× 45 1.4k
Laura E. Brown United States 14 1.0k 1.6× 238 0.9× 173 0.7× 16 0.1× 7 0.0× 41 1.9k
Meng Han China 13 438 0.7× 163 0.6× 95 0.4× 11 0.1× 42 0.3× 50 1.1k
Fernando Díaz Spain 19 589 0.9× 164 0.6× 178 0.7× 6 0.0× 28 0.2× 64 1.2k

Countries citing papers authored by Tome Eftimov

Since Specialization
Citations

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

Fields of papers citing papers by Tome Eftimov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tome Eftimov

This figure shows the co-authorship network connecting the top 25 collaborators of Tome Eftimov. A scholar is included among the top collaborators of Tome Eftimov 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 Tome Eftimov. Tome Eftimov 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.
Muñoz, Mario Andrés, et al.. (2025). Benchmarking footprints of continuous black-box optimization algorithms: Explainable insights into algorithm success and failure. Swarm and Evolutionary Computation. 94. 101895–101895.
2.
Doerr, Carola, et al.. (2025). Geometric Learning in Black-Box Optimization: A GNN Framework for Algorithm Performance Prediction. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 487–490.
3.
4.
Eftimov, Tome, et al.. (2025). A learning search algorithm for the Restricted Longest Common Subsequence problem. Expert Systems with Applications. 284. 127731–127731.
5.
6.
Seljak, Barbara Koroušić, et al.. (2024). Zero-shot evaluation of ChatGPT for food named-entity recognition and linking. Frontiers in Nutrition. 11. 1429259–1429259. 3 indexed citations
7.
Eftimov, Tome, et al.. (2024). TransOptAS: Transformer-Based Algorithm Selection for Single-Objective Optimization. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 403–406. 1 indexed citations
8.
Vermetten, Diederick, et al.. (2024). Using Machine Learning Methods to Assess Module Performance Contribution in Modular Optimization Frameworks. Evolutionary Computation. 33(4). 485–512. 3 indexed citations
9.
Doerr, Carola, et al.. (2024). Generalization Ability of Feature-Based Performance Prediction Models: A Statistical Analysis Across Benchmarks. SPIRE - Sciences Po Institutional REpository. 1–8. 1 indexed citations
10.
Pravst, Igor, et al.. (2024). NutriGreen image dataset: a collection of annotated nutrition, organic, and vegan food products. Frontiers in Nutrition. 11. 1342823–1342823. 1 indexed citations
11.
Džeroski, Sašo, et al.. (2023). Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem Instances. Proceedings of the Genetic and Evolutionary Computation Conference. 529–537. 4 indexed citations
12.
Doerr, Carola, et al.. (2023). DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems. Proceedings of the Genetic and Evolutionary Computation Conference. 813–821. 3 indexed citations
13.
Vermetten, Diederick, et al.. (2022). The importance of landscape features for performance prediction of modular CMA-ES variants. Proceedings of the Genetic and Evolutionary Computation Conference. 648–656. 5 indexed citations
14.
Doerr, Carola, et al.. (2022). Explainable Model-specific Algorithm Selection for Multi-Label Classification. SPIRE - Sciences Po Institutional REpository. 39–46. 2 indexed citations
15.
Vermetten, Diederick, et al.. (2022). OPTION: OPTImization Algorithm Benchmarking ONtology. IEEE Transactions on Evolutionary Computation. 27(6). 1618–1632. 5 indexed citations
16.
Popovski, Gorjan, et al.. (2020). Food Data Integration by using Heuristics based on Lexical and Semantic Similarities. 208–216. 1 indexed citations
17.
Eftimov, Tome, Gorjan Popovski, Matej Petković, Barbara Koroušić Seljak, & Dragi Kocev. (2020). COVID-19 pandemic changes the food consumption patterns. Trends in Food Science & Technology. 104. 268–272. 130 indexed citations
18.
Eftimov, Tome, et al.. (2020). Evaluating missing value imputation methods for food composition databases. Food and Chemical Toxicology. 141. 111368–111368. 37 indexed citations
19.
Popovski, Gorjan, et al.. (2019). FoodIE: A Rule-based Named-entity Recognition Method for Food Information Extraction. 915–922. 34 indexed citations
20.
Eftimov, Tome & Dragi Kocev. (2019). Performance Measures Fusion for Experimental Comparison of Methods for Multi-label Classification.. 5 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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