| An effective PSO-inspired algorithm for the team orienteering problem DC Dang, RN Guibadj, A Moukrim European Journal of Operational Research 229 (2), 332-344, 2013 | 166 | 2013 |
| Escaping local optima using crossover with emergent diversity DC Dang, T Friedrich, T Kötzing, MS Krejca, PK Lehre, PS Oliveto, ... IEEE Transactions on Evolutionary Computation 22 (3), 484-497, 2017 | 145 | 2017 |
| Level-based analysis of genetic algorithms and other search processes D Corus, DC Dang, AV Eremeev, PK Lehre IEEE Transactions on Evolutionary Computation 22 (5), 707-719, 2017 | 134 | 2017 |
| Heuristic solutions for the vehicle routing problem with time windows and synchronized visits S Afifi, DC Dang, A Moukrim Optimization Letters 10, 511-525, 2016 | 107 | 2016 |
| A memetic algorithm for the team orienteering problem H Bouly, DC Dang, A Moukrim 4or 8, 49-70, 2010 | 99 | 2010 |
| Self-adaptation of mutation rates in non-elitist populations DC Dang, PK Lehre Parallel Problem Solving from Nature–PPSN XIV: 14th International Conference …, 2016 | 90 | 2016 |
| A simulated annealing algorithm for the vehicle routing problem with time windows and synchronization constraints S Afifi, DC Dang, A Moukrim Learning and Intelligent Optimization: 7th International Conference, LION 7 …, 2013 | 77 | 2013 |
| Escaping local optima with diversity mechanisms and crossover DC Dang, T Friedrich, T Kötzing, MS Krejca, PK Lehre, PS Oliveto, ... Proceedings of the Genetic and Evolutionary Computation Conference 2016, 645-652, 2016 | 76 | 2016 |
| A branch-and-cut algorithm for solving the team orienteering problem DC Dang, R El-Hajj, A Moukrim Integration of AI and OR Techniques in Constraint Programming for …, 2013 | 71 | 2013 |
| Solving the team orienteering problem with cutting planes R El-Hajj, DC Dang, A Moukrim Computers & Operations Research 74, 21-30, 2016 | 70 | 2016 |
| Runtime analysis of non-elitist populations: from classical optimisation to partial information DC Dang, PK Lehre Algorithmica 75, 428-461, 2016 | 70 | 2016 |
| Efficient optimisation of noisy fitness functions with population-based evolutionary algorithms DC Dang, PK Lehre Proceedings of the 2015 ACM Conference on Foundations of Genetic Algorithms …, 2015 | 58 | 2015 |
| A pso-based memetic algorithm for the team orienteering problem DC Dang, RN Guibadj, A Moukrim Applications of Evolutionary Computation: EvoApplications 2011: EvoCOMNET …, 2011 | 52 | 2011 |
| Level-based analysis of genetic algorithms and other search processes D Corus, DC Dang, AV Eremeev, PK Lehre International Conference on Parallel Problem Solving from Nature, 912-921, 2014 | 46 | 2014 |
| Simplified runtime analysis of estimation of distribution algorithms DC Dang, PK Lehre Proceedings of the 2015 Annual Conference on Genetic and Evolutionary …, 2015 | 43 | 2015 |
| Escaping local optima with non-elitist evolutionary algorithms DC Dang, A Eremeev, PK Lehre Proceedings of the AAAI Conference on Artificial Intelligence 35 (14), 12275 …, 2021 | 42 | 2021 |
| Level-based analysis of the univariate marginal distribution algorithm DC Dang, PK Lehre, PTH Nguyen Algorithmica 81, 668-702, 2019 | 37 | 2019 |
| A proof that using crossover can guarantee exponential speed-ups in evolutionary multi-objective optimisation DC Dang, A Opris, B Salehi, D Sudholt Proceedings of the AAAI Conference on Artificial Intelligence 37 (10), 12390 …, 2023 | 36 | 2023 |
| Populations can be essential in tracking dynamic optima DC Dang, T Jansen, PK Lehre Algorithmica 78, 660-680, 2017 | 35 | 2017 |
| Toward a unifying framework for evolutionary processes T Paixăo, G Badkobeh, N Barton, D Çörüş, DC Dang, T Friedrich, ... Journal of Theoretical Biology 383, 28-43, 2015 | 33 | 2015 |
Per Kristian LehreProfessor at University of Birmingham and UKRI Turing AI Acceleration FellowVerified email at bham.ac.uk
