Dr Nadarajen Veerapen
I am a Postdoctoral Research Assistant working on the DAASE project funded by the EPSRC and on the Cartography of computational search spaces funded by the Leverhulme Trust. I have been part of the CHORDS group at the University of Stirling since September 2013. I'm interested in meta and hyperheuristics but I do like to use exact methods when possible.
My current work revolves around several topics:
- Requirements optimisation — what requirements should be given priority in order to minimize cost or development time while making the stakeholders asking for requirements happy?
- Search landscape visualisation — visualisations help us to understand both the problems and how the solving methods are able, or not, to deal with them.
- Hyper-heuristics — performing optimisation in the heuristic space in order to enhance the genericity of solving methods and make them easier to use by a wider public.
- Optimising energy consumption — software can be designed, or modified, to improve its energy consumption on platforms such as embedded systems or mobile devices.
- Spring 2016 — CSCUT4 — Lectures on Computer Security
- Spring 2016 — ITNPBD8/CSCU9YO — Lecture on Multi-objective Optimisation
- Autumn 2015 — CSCU9YE — Artificial Intelligence (Demonstrations)
- Spring 2015 — ITNPBD8/CSCU9YO — Invited lecture on Multi-objective Optimisation
- Co-organiser of the Workshop on Landscape-Aware Heuristic Search, 17-18 September, PPSN 2016.
- Co-organiser of the SICSA CSE Workshop on modelling and optimisation of real-world transportation problems (Jan 2015).
- Program Committee member for GECCO 2016, EvoCOP 2016, EvoINDUSTRY 2016, PPSN 2016, JFPC 2016, CP 2015 Doctoral Program, GECCO 2015, EvoCOP 2015, JFPC 2015, EvoCOP 2014, JFPC 2014 and GECCO 2013.
- Reviewer for IEEE Transactions on Software Engineering, Evolutionary Computation and the Journal of Systems and Software.
- External reviewer for EA 2015, UKCI 2015, PPSN 2014, PPSN 2012 and CP 2011.
I obtained my Licence (Bachelor's degree) in Computer Science from the University of Nantes, France, and carried on there with an International Master's degree in Computer Science specialising in Optimisation in Operations Research. My research work for my Master's was carried out at the University of Nottingham and involved developing a tabu-based hyper-heuristic for multi-objective optimisation.
In 2012 I received my PhD, funded by a Microsoft Research Scholarship, from the University of Angers, France. I worked on autonomous operator control for local search and focused on combinatorial optimisation problems. Half-way through my PhD, I had the opportunity to be a visiting researcher at the Austral University of Chile in Valdivia for one month. During my time in Angers, I was also a teaching assistant and mainly taught functional programming and introduction to algorithms.
- N. Veerapen, G. Ochoa, M. Harman and E. K. Burke. An Integer Linear Programming approach to the single and bi-objective Next Release Problem. Information and Software Technology, Volume 65, September 2015, Pages 1-13, ISSN 0950-5849. DOI:10.1016/j.infsof.2015.03.008
- N. Veerapen, G. Ochoa, R. Tinós, D. Whitley. Tunnelling Crossover Networks for the Asymmetric TSP. The 14th International Conference on Parallel Problem Solving from Nature, PPSN2016, 17-21 September 2016, Edinburgh, Scotland. (accepted)
- G. Ochoa, N. Veerapen. Additional Dimensions to the Study of Funnels in Combinatorial Landscapes. The 2016 Genetic and Evolutionary Computation Conference, GECCO 2016, 20-24 July 2016, Denver, Colorado, USA. [dataset] (to appear)
- G. Ochoa, N. Veerapen. Deconstructing the Big Valley Search Space Hypothesis. Evolutionary Computation in Combinatorial Optimization, Proceedings of the 16th European Conference, EvoCOP 2016, Lecture Notes in Computer Science, vol. 9595, pp. 58–73. Springer International Publishing, 2016. DOI:10.1007/978-3-319-30698-8_5 [dataset] [poster] (Best Paper Award)
- G. Ochoa, N. Veerapen, D. Whitley and E. Burke. The Multi-Funnel Structure of TSP Fitness Landscapes: A Visual Exploration. Artificial Evolution: 12th International Conference, Evolution Artificielle, EA 2015, Lecture Notes in Computer Science, vol. 9554, pp. 1–13. Springer International Publishing, 2016. DOI:10.1007/978-3-319-31471-6_1 [dataset]
- N. Burles, E. Bowles, A. E. I. Brownlee, Z. A. Kocsis, J. Swan, N. Veerapen. Object-Oriented Genetic Improvement for Improved Energy Consumption in Google Guava. In M. Barros and Y. Labiche, editors, Search-Based Software Engineering, Lecture Notes in Computer Science, vol. 9275, pp. 255-61. Springer International Publishing, 2015. DOI:10.1007/978-3-319-22183-0_20
- N. Veerapen, Y. Hamadi and F. Saubion. Using Local Search with adaptive operator selection to solve the Progressive Party Problem. In Proceedings of the IEEE Congress on Evolutionary Computation 2013 (CEC 2013), pp. 554–561, 2013. DOI:10.1109/CEC.2013.6557617
- N. Veerapen, J. Maturana and F. Saubion. An Exploration-Exploitation Compromise-Based Adaptive Operator Selection for Local Search. In T. Soule, editor, Proceedings of the Fourteenth International Genetic and Evolutionary Computation Conference (GECCO 2012) , pp. 1277–1284. ACM, New York, NY, USA, 2012. DOI:10.1145/2330163.2330340
- N. Veerapen, J. Maturana and F. Saubion. A Comparison of Operator Utility Measures for On-line Operator Selection in Local Search. In Y. Hamadi et M. Schoenauer, editors, Learning and Intelligent Optimization, Proceedings of the Sixth Learning and Intelligent Optimization Conference (LION6), Lecture Notes in Computer Science, vol. 7219, pp. 497–502. Springer Berlin / Heidelberg, 2012. DOI:10.1007/978-3-642-34413-8_51
- N. Veerapen, and F. Saubion. Pareto Autonomous Local Search. In C.A. Coello Coello, editor, Learning and Intelligent Optimization, Proceedings of the Fifth Learning and Intelligent Optimization Conference (LION5), Lecture Notes in Computer Science, vol. 6683, pp. 392–406. Springer Berlin / Heidelberg, 2011. DOI:10.1007/978-3-642-25566-3_29
- N. Burles, J. Swan, E. Bowles, A. E. I. Brownlee, Z. A. Kocsis, N. Veerapen. Embedded Dynamic Improvement. In Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference (GECCO Companion '15), pp. 831–32. ACM, New York, NY, USA, 2015. DOI:10.1145/2739482.2768423
- N. Veerapen, D. Landa-Silva and X. Gandibleux. Hyperheuristic as Component of a Multi-Objective Metaheuristic. In SLS-DS 2009: Doctoral Symposium on Engineering Stochastic Local Search Algorithms, Technical Report TR/IRIDIA/2009-024, IRIDIA, Université Libre de Bruxelles, Brussels, Belgium, September 2009.
- N. Veerapen, Y. Hamadi and F. Saubion. Sélection adaptative d'opérateurs pour la recherche locale et Progressive Party Problem. In Actes des Neuvièmes Journées Francophones de Programmation par Contraintes (JFPC 2013). Aix-en-Provence, France, June 2013.
- N. Veerapen, J. Maturana and F. Saubion. Sélection adaptative d'opérateurs pour la recherche locale basée sur un compromis exploration-exploitation. In Actes des Huitièmes Journées Francophones de Programmation par Contraintes (JFPC 2012), pp. 318–327. Toulouse, France, May 2012.
- N. Veerapen and F. Saubion. Sélection autonome d'opérateurs par dominance pour la recherche locale. In Actes des Septièmes Journées Francophones de Programmation par Contraintes (JFPC 2011), pp. 307–316. Lyon, France, June 2011.
- N. Veerapen. Contrôle autonome d'opérateurs pour la recherche locale, PhD Thesis, Université d'Angers, Angers, France, November 2012. French Theses Open Archive
- N. Veerapen. A Heuristic Selection Mechanism to Improve the Distribution of Non-dominated Fronts for the Multi-objective TSP. Master's Thesis, Université de Nantes, Nantes, France, July 2009.