Tae Jong Choi

481 total citations
30 papers, 230 citations indexed

About

Tae Jong Choi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics. According to data from OpenAlex, Tae Jong Choi has authored 30 papers receiving a total of 230 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Artificial Intelligence, 9 papers in Computer Vision and Pattern Recognition and 9 papers in Computational Theory and Mathematics. Recurrent topics in Tae Jong Choi's work include Metaheuristic Optimization Algorithms Research (15 papers), Evolutionary Algorithms and Applications (14 papers) and Advanced Multi-Objective Optimization Algorithms (9 papers). Tae Jong Choi is often cited by papers focused on Metaheuristic Optimization Algorithms Research (15 papers), Evolutionary Algorithms and Applications (14 papers) and Advanced Multi-Objective Optimization Algorithms (9 papers). Tae Jong Choi collaborates with scholars based in South Korea, Vietnam and United States. Tae Jong Choi's co-authors include Chang Wook Ahn, Julian Togelius, Jinung An, Yun-Gyung Cheong, Nikhil Pachauri, Hee Yong Youn, Jong‐Hyun Lee, Lifan Wang, Hae‐Gon Jeon and Son Nguyen and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and IEEE Access.

In The Last Decade

Tae Jong Choi

26 papers receiving 223 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tae Jong Choi South Korea 10 126 62 45 36 24 30 230
Rui Zhong Japan 11 191 1.5× 78 1.3× 39 0.9× 39 1.1× 24 1.0× 47 295
Rawaa Dawoud Al-Dabbagh Iraq 6 178 1.4× 119 1.9× 16 0.4× 28 0.8× 45 1.9× 15 299
P. Shanthi Bala India 8 109 0.9× 47 0.8× 45 1.0× 19 0.5× 12 0.5× 19 187
Tomáš Kadavý Czechia 8 209 1.7× 122 2.0× 19 0.4× 22 0.6× 31 1.3× 49 278
Christian L. Camacho‐Villalón Belgium 6 184 1.5× 107 1.7× 26 0.6× 24 0.7× 23 1.0× 7 288
Matthieu Geist France 10 176 1.4× 20 0.3× 29 0.6× 25 0.7× 52 2.2× 27 261
Mathys C. du Plessis South Africa 11 240 1.9× 101 1.6× 40 0.9× 14 0.4× 51 2.1× 35 315
Scott Fujimoto Canada 3 114 0.9× 23 0.4× 36 0.8× 38 1.1× 51 2.1× 6 191
Ye Miao China 7 134 1.1× 115 1.9× 14 0.3× 54 1.5× 19 0.8× 13 266
Patricia Ochoa Mexico 10 228 1.8× 37 0.6× 25 0.6× 28 0.8× 81 3.4× 22 312

Countries citing papers authored by Tae Jong Choi

Since Specialization
Citations

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

Fields of papers citing papers by Tae Jong Choi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tae Jong Choi. 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 Tae Jong Choi. The network helps show where Tae Jong Choi may publish in the future.

Co-authorship network of co-authors of Tae Jong Choi

This figure shows the co-authorship network connecting the top 25 collaborators of Tae Jong Choi. A scholar is included among the top collaborators of Tae Jong Choi 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 Tae Jong Choi. Tae Jong Choi 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.
Choi, Tae Jong, et al.. (2025). CLEAR: Cross-Transformers With Pre-Trained Language Model for Person Attribute Recognition and Retrieval. Pattern Recognition. 164. 111486–111486.
2.
Choi, Tae Jong. (2025). An efficient eigenvector-based crossover for differential evolution: Simplifying with rank-one updates. AIMS Mathematics. 10(2). 3500–3522. 1 indexed citations
3.
Choi, Tae Jong, et al.. (2025). Towards enhancing prototypes driven by graph convolutional network for domain adaptation. Expert Systems with Applications. 299. 130010–130010.
4.
Choi, Tae Jong, et al.. (2025). Cross-domain knowledge distillation for domain adaptation with GCN-driven MLP generalization. Applied Soft Computing. 184. 113771–113771.
5.
Pachauri, Nikhil, Chang Wook Ahn, & Tae Jong Choi. (2025). Biochar energy prediction from different biomass feedstocks for clean energy generation. Environmental Technology & Innovation. 37. 104012–104012. 4 indexed citations
6.
Choi, Tae Jong, et al.. (2025). How to enrich cross-domain representations? Data augmentation, cycle-pseudo labeling, and category-aware graph learning. Expert Systems with Applications. 271. 126597–126597. 1 indexed citations
7.
Choi, Tae Jong & Nikhil Pachauri. (2024). Adaptive search space for stochastic opposition-based learning in differential evolution. Knowledge-Based Systems. 300. 112172–112172. 5 indexed citations
8.
Jeon, Hae‐Gon, et al.. (2024). Learning CNN on ViT: A Hybrid Model to Explicitly Class-Specific Boundaries for Domain Adaptation. 28545–28554. 10 indexed citations
9.
Choi, Tae Jong, et al.. (2024). Dual Dynamic Consistency Regularization for Semi-Supervised Domain Adaptation. IEEE Access. 12. 36267–36279. 8 indexed citations
10.
Pachauri, Nikhil, Chang Wook Ahn, & Tae Jong Choi. (2023). A blended ensemble model for biomass HHV prediction from ultimate analysis. Fuel. 357. 129898–129898. 7 indexed citations
11.
Choi, Tae Jong. (2023). A rotationally invariant stochastic opposition-based learning using a beta distribution in differential evolution. Expert Systems with Applications. 231. 120658–120658. 5 indexed citations
12.
Choi, Tae Jong, et al.. (2022). StereoPairFree: Self-Constructed Stereo Correspondence Network From Natural Images. IEEE Intelligent Systems. 38(1). 19–33. 3 indexed citations
13.
Choi, Tae Jong & Julian Togelius. (2021). Self-referential quality diversity through differential MAP-Elites. Proceedings of the Genetic and Evolutionary Computation Conference. 502–509. 4 indexed citations
14.
Choi, Tae Jong, Julian Togelius, & Yun-Gyung Cheong. (2020). Advanced Cauchy Mutation for Differential Evolution in Numerical Optimization. IEEE Access. 8. 8720–8734. 23 indexed citations
15.
Choi, Tae Jong, Jong‐Hyun Lee, Hee Yong Youn, & Chang Wook Ahn. (2019). Adaptive Differential Evolution with Elite Opposition-Based Learning and its Application to Training Artificial Neural Networks. Fundamenta Informaticae. 164(2-3). 227–242. 12 indexed citations
16.
Choi, Tae Jong & Chang Wook Ahn. (2018). Accelerating differential evolution using multiple exponential cauchy mutation. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 207–208. 3 indexed citations
17.
Choi, Tae Jong & Chang Wook Ahn. (2017). A Swarm Art Based on Evolvable Boids with Genetic Programming. Journal of Advances in Information Technology. 23–28.
18.
Choi, Tae Jong & Chang Wook Ahn. (2017). Artificial life based on boids model and evolutionary chaotic neural networks for creating artworks. Swarm and Evolutionary Computation. 47. 80–88. 12 indexed citations
19.
Choi, Tae Jong & Chang Wook Ahn. (2016). Adaptive α-stable differential evolution in numerical optimization. Natural Computing. 16(4). 637–657. 5 indexed citations
20.
Choi, Tae Jong, Chang Wook Ahn, & Jinung An. (2013). An Adaptive Cauchy Differential Evolution Algorithm for Global Numerical Optimization. The Scientific World JOURNAL. 2013(1). 969734–969734. 32 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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