Publications

2025

Nguyen Dang, Ian P. Gent, Peter Nightingale, Felix Ulrich-Oltean, and Jack Waller. Constraint Models for Klondike. CP’2025
Alessio Pellegrino, Özgür Akgün, Nguyen Dang, Zeynep Kiziltan, and Ian Miguel. Transformer-Based Feature Learning for Algorithm Selection in Combinatorial Optimisation. CP’2025
Tai Nguyen, Phong Le, André Biedenkapp, Carola Doerr and Nguyen Dang. On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+($\lambda$,$\lambda$))-GA. Best paper award at GECCO’2025 (L4EC track) [arXiv] [doi]
Tai Nguyen, Phong Le, Carola Doerr and Nguyen Dang. Multi-parameter Control for the (1+(λ,λ))-GA on OneMax via Deep Reinforcement Learning”. FOGA’2025 [arXiv]
Saad Attieh, Nguyen Dang, Chris Jefferson, Ian Miguel, and Peter Nightingale. Athanor: Local search over abstract constraint specifications. Artificial Intelligence Journal [doi]

Alessio Pellegrino, Özgür Akgün, Nguyen Dang, Zeynep Kiziltan, and Ian Miguel. Automatic Feature Learning for Essence: a Case Study on Car Sequencing. ModRef’2024
Erdem Kuş, Özgür Akgün, Nguyen Dang, and Ian Miguel Frugal Algorithm Selection. CP’2024
Nguyen Dang, Ian Gent, Peter Nightingale, Felix Ulrich-Oltean, and Jack Waller. Constraint models for relaxed Klondike variants. ModRef’2024

Patrick Spracklen, Nguyen Dang, Özgür Akgün, and Ian Miguel. Automated streamliner portfolios for constraint satisfaction problems. Artificial Intelligence Journal
Deyao Chen, Maxim Buzdalov, Carola Doerr, and Nguyen Dang. Using Automated Algorithm Configuration for Parameter Control. FOGA 2023 [arXiv] [doi] Nominated for best paper award

Nguyen Dang, Özgür Akgün, Joan Espasa, Ian Miguel, and Peter Nightingale. A Framework for Generating Informative Benchmark Instances. CP 2022
André Biedenkapp, Nguyen Dang, Martin S. Krejca, Frank Hutter, and Carola Doerr. Theory‑inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration. Best paper award at GECCO’2022 (GECH track) (shared first authorship)
Nguyen Dang. A portfolio-based analysis method for competition results. ModRef’2022 [arXiv]

Özgür Akgün, Nguyen Dang, Ian Miguel, András Z. Salamon, Patrick Spracklen, and Christopher Stone. Discriminating Instance Generation from Abstract Specifications: A Case Study with CP and MIP. CPAIOR’2020 [pdf]
Patrick Spracklen, Nguyen Dang, Özgür Akgün, and Ian Miguel. Towards Portfolios of Streamlined Constraint Models: A Case Study with the Balanced Academic Curriculum Problem. ModRef’2021 [pdf]
Özgür Akgün, Nguyen Dang, Joan Espasa, Ian Miguel, András Z. Salamon, and Christopher Stone. Exploring Instance Generation for Automated Planning. ModRef’2021 [pdf]
Gökberk Koçak, Özgür Akgün, Nguyen Dang, and Ian Miguel. Efficient Incremental Modelling and Solving. ModRef’2021 [pdf]

Sara Ceschia, Nguyen Dang, Patrick De Causmaecker, Stefaan Haspeslagh, and Andrea Schaerf. The Second International Nurse Rostering Competition. Annals of Operations Research 
Jeroen Corstjens, Nguyen Dang, Benoît Depaire, An Caris, and Patrick De Causmaecker. A Combined Approach for Analysing Heuristic Algorithms. Journal of Heuristics 
Özgür Akgün, Nguyen Dang, Ian Miguel, András Z. Salamon, and Christopher Stone. Instance Generation via Generator Instances. CP’2019
Carlos Ansótegui, Miquel Bofill, Jordi Coll, Nguyen Dang, Juan Luis Esteban, Ian Miguel, Peter Nightingale, András Z. Salamon, Josep Suy, and Mateu Villaret. Automatic detection of at-most-one and exactly-one relations for improved SAT encodings of pseudo-boolean constraints. CP’2019
Patrick Spracklen, Nguyen Dang, Özgür Akgün, and Ian Miguel. Automatic Streamlining for Constrained Optimisation. CP’2019
Nguyen Dang and Carola Doerr. Hyper‑parameter tuning for the (1 + (λ, λ)) GA. GECCO’2019
Saad Attieh, Nguyen Dang, Christopher Jefferson, Ian Miguel, and Peter Nightingale. Athanor: High‑Level Local Search Over Abstract Constraint Specifications in Essence. IJCAI’2019

Nguyen Dang and Patrick De Causmaecker. Analysis of Algorithm Components and Parameters: Some Case Studies. LION’2018

Nguyen Dang, Leslie Pérez Cáceres, Patrick De Causmaecker, and Thomas Stützle. Configuring irace using surrogate configuration benchmarks. Best paper award at GECCO’2017 (ECOM track) [doi]

Nguyen Dang, Sara Ceschia, Andrea Schaerf, Patrick De Causmaecker, and Stefaan Haspeslagh. Solving the multi-stage nurse rostering problem. PATAT’2016
Nguyen Dang and Patrick De Causmaecker. Characterization of Neighborhood Behaviours in a Multi‑neighborhood Local Search Algorithm. LION 2016
Niels Elgers, Nguyen Dang and Patrick De Causmaecker. A Metaheuristic Approach to computing (approximate) Pure Nash Equilibria. META’2016
Nguyen Dang and Patrick De Causmaecker. Characterization of neighborhood behaviours in a multi-neighborhood local search algorithm. LION’2016

Nguyen Dang and Tien Dinh. A multi-stage local search for a real-world vehicle routing problem. IEEE ICNC’2011
Khoa Trinh, Nguyen Dang, and Tien Dinh. An Approximation Approach for a Real–World Variant of Vehicle Routing Problem. In New Challenges for Intelligent Information and Database Systems.