Recent publications
Article
Case, B & Lehre, PK 2020, 'Self-adaptation in non-elitist evolutionary algorithms on discrete problems with unknown structure', IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/TEVC.2020.2985450
Lehre, PK & Witt, C 2020, 'Tail bounds on hitting times of randomized search heuristics using variable drift analysis', Combinatorics, Probability and Computing. https://doi.org/10.1017/S0963548320000565
Dang, D-C, Lehre, PK & Nguyen, PTH 2019, 'Level-based analysis of the univariate marginal distribution algorithm', Algorithmica, vol. 81, no. 2, pp. 668-702. https://doi.org/10.1007/s00453-018-0507-5
Lehre, PK & Sudholt, D 2019, 'Parallel black-box complexity with tail bounds', IEEE Transactions on Evolutionary Computation, pp. 1-15. https://doi.org/10.1109/TEVC.2019.2954234
Trubenová, B, Krejca, MS, Lehre, PK & Kötzing, T 2019, 'Surfing on the seascape: adaptation in a changing environment', Evolution; international journal of organic evolution, vol. 73, no. 7, pp. 1356-1374. https://doi.org/10.1111/evo.13784
Dang, D-C, Friedrich, T, Koetzing, T, Krejca, MS, Lehre, P, Oliveto, PS, Sudholt, D & Sutton, AM 2018, 'Escaping local optima using crossover with emergent diversity', IEEE Transactions on Evolutionary Computation, vol. 22, no. 3, pp. 484 - 497. https://doi.org/10.1109/TEVC.2017.2724201
Corus, D, Dang, D-C, Eremeev, AV & Lehre, PK 2018, 'Level-based analysis of genetic algorithms and other search processes', IEEE Transactions on Evolutionary Computation, vol. 22, no. 5, pp. 707 - 719. https://doi.org/10.1109/TEVC.2017.2753538
Corus, D & Lehre, PK 2018, 'Theory driven design of efficient genetic algorithms for a classical graph problem', Operations Research/ Computer Science Interfaces Series, vol. 62, pp. 125-140. https://doi.org/10.1007/978-3-319-58253-5_8
Dang, DC, Jansen, T & Lehre, P 2017, 'Populations Can Be Essential in Tracking Dynamic Optima', Algorithmica, vol. 78, no. 2, pp. 660-680. https://doi.org/10.1007/s00453-016-0187-y
Corus, D, Lehre, PK, Neumann, F & Pourhassan, M 2016, 'A parameterised complexity analysis of bi-level optimisation with evolutionary algorithms', Evolutionary Computation, vol. 24, no. 1, pp. 183-203. https://doi.org/10.1162/EVCO_a_00147
Conference contribution
Lehre, PK & Nguyen, PTH 2019, On the limitations of the univariate marginal distribution algorithm to deception and where bivariate EDAs might help. in Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA '19). Association for Computing Machinery (ACM), New York, NY, USA, pp. 154-168, 15th ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA XV), Potsdam, Germany, 27/08/19. https://doi.org/10.1145/3299904.3340316
Lehre, PK & Nguyen, PTH 2019, Runtime analysis of the univariate marginal distribution algorithm under low selective pressure and prior noise. in M López-Ibáñez (ed.), The Genetic and Evolutionary Computation Conference 2019 (GECCO 2019). GECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference, Association for Computing Machinery (ACM), pp. 1497-1505, The Genetic and Evolutionary Computation Conference 2019 (GECCO 2019), Prague, Czech Republic, 13/07/19. https://doi.org/10.1145/3321707.3321834
Lehre, PK & Nguyen, PTH 2018, Level-based analysis of the population-based incremental learning algorithm. in Proceedings of the 15th International Conference on Parallel Problem Solving from Nature 2018 (PPSN XV). 1 edn, vol. 11101, Lecture Notes in Computer Science, Springer, 15th International Conference on Parallel Problem Solving from Nature 2018 (PPSN XV), Coimbra, Portugal, 8/09/18. https://doi.org/10.1007/978-3-319-99253-2
Lehre, PK & Nguyen, PTH 2017, Improved runtime bounds for the univariate marginal distribution algorithm via anti-concentration. in GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery , pp. 1383-1390, GECCO 2017:, Berlin, Germany, 15/07/17. https://doi.org/10.1145/3071178.3071317
Dang, DC, Friedrich, T, Kötzing, T, Krejca, MS, Lehre, PK, Oliveto, PS, Sudholt, D & Sutton, AM 2016, Emergence of diversity and its benefits for crossover in genetic algorithms. in Parallel Problem Solving from Nature - 14th International Conference, PPSN 2016, Proceedings. vol. 9921 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9921 LNCS, Springer Verlag, pp. 890-900, 14th International Conference on Parallel Problem Solving from Nature, PPSN 2016, Edinburgh, United Kingdom, 17/09/16. https://doi.org/10.1007/978-3-319-45823-6_83
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