Research areas
machine learning and deep reinforcement learning for automated algorithm design, constraint programming, combinatorial optimisation, evolutionary algorithms.
Selected publications
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Open access
Automated streamliner portfolios for constraint satisfaction problems
Spracklen, J. L. P. J., Dang, N., Akgun, O. & Miguel, I. J., 1 Jun 2023, In: Artificial Intelligence. 319, 24 p., 103915.Research output: Contribution to journal › Article › peer-review
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Using automated algorithm configuration for parameter control
Chen, D., Buzdalov, M., Doerr, C. & Dang, N., 30 Aug 2023, FOGA'23: proceedings of the 17th ACM/SIGEVO conference on Foundations of Genetic Algorithms. Chicano, F., Friedrich, T., Kötzing, T. & Rothlauf, F. (eds.). New York, NY: ACM, p. 38-49 12 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Open access
A framework for generating informative benchmark instances
Dang, N., Akgun, O., Espasa Arxer, J., Miguel, I. J. & Nightingale, P., 23 Jul 2022, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Solon, C. (ed.). Dagstuhl: Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 18 p. 18. (Leibniz International Proceedings in Informatics (LIPIcs); vol. 235).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Open access
A portfolio-based analysis method for competition results
Dang, N., 31 Jul 2022, ModRef 2022: 21st workshop on constraint modelling and reformulation. Online, 11 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Open access
Theory-inspired parameter control benchmarks for dynamic algorithm configuration
Biedenkapp, A., Dang, N., Krejca, M., Hutter, F. & Doerr, C., 8 Jul 2022, GECCO '22: Proceedings of the genetic and evolutionary computation conference. Fieldsend, J. E. (ed.). New York, NY: ACM, p. 766–775 10 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Open access
Discriminating instance generation from abstract specifications: a case study with CP and MIP
Akgün, Ö., Dang, N., Miguel, I., Salamon, A. Z., Spracklen, P. & Stone, C., 2020, Integration of Constraint Programming, Artificial Intelligence, and Operations Research: 17th International Conference, CPAIOR 2020, Vienna, Austria, September 21–24, 2020, Proceedings. Hebrard, E. & Musliu, N. (eds.). Cham: Springer, p. 41-51 11 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12296 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Open access
Efficient incremental modelling and solving
Koçak, G., Akgün, Ö., Dang, N. & Miguel, I., 7 Sept 2020, ModRef 2020 - The 19th workshop on Constraint Modelling and Reformulation. 15 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Open access
Exploring instance generation for automated planning
Akgün, Ö., Dang, N., Espasa, J., Miguel, I., Salamon, A. Z. & Stone, C., 7 Sept 2020, ModRef 2020 - The 19th workshop on Constraint Modelling and Reformulation. 17 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Open access
Towards portfolios of streamlined constraint models: a case study with the balanced academic curriculum problem
Spracklen, P., Dang, N., Akgün, Ö. & Miguel, I., 7 Sept 2020, ModRef 2020 - The 19th workshop on Constraint Modelling and Reformulation. 17 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Open access
A combined approach for analysing heuristic algorithms
Jeroen Corstjens, Dang, N., Depaire, B., Caris, A. & De Causmaeckers, P., 1 Oct 2019, In: Journal of Heuristics. 25, 10, p. 591–628 38 p.Research output: Contribution to journal › Article › peer-review