Professor Per Kristian Lehre

Professor Per Kristian Lehre

School of Computer Science
Professor of Evolutionary Computation

Contact details

Address
School of Computer Science
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Professor Per Kristian Lehre is a professor of evolutionary computation in the School of Computer Science at the University of Birmingham. For more information, please see Professor Per Kristian's homepage.

Teaching

  • Nature Inspired Search and Optimisation (Spring 2020, co-taught with Shan He)
  • Neural Computation (Autumn 2019, co-taught with Jinming Duan)

Publications

Recent publications

Article

Lehre, PK & Qin, X 2022, 'More precise runtime analyses of non-elitist evolutionary algorithms in uncertain environments', Algorithmica. https://doi.org/10.1007/s00453-022-01044-5

Lehre, PK & Nguyen, H 2021, 'Runtime analyses of the population-based univariate estimation of distribution algorithms on LeadingOnes', Algorithmica, vol. 83, no. 10, pp. 3238-3280. https://doi.org/10.1007/s00453-021-00862-3

Case, B & Lehre, PK 2020, 'Self-adaptation in non-elitist evolutionary algorithms on discrete problems with unknown structure', IEEE Transactions on Evolutionary Computation, vol. 24, no. 4, 9056791, pp. 650-663. 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

Chapter (peer-reviewed)

Dang, D-C, Eremeev, AV & Lehre, PK 2020, Escaping Local Optima with Non-Elitist Evolutionary Algorithms. in Proceedings of AAAI 2021. AAAI Press.

Conference contribution

Dang, D-C, Eremeev, A, Lehre, PK & Qin, X 2022, Fast non-elitist evolutionary algorithms with power-law ranking selection. in JE Fieldsend (ed.), GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO: Genetic and Evolutionary Computation Conference, Association for Computing Machinery (ACM), New York, pp. 1372-1380, GECCO '22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, United States, 9/07/22. https://doi.org/10.1145/3512290.3528873

Lehre, PK 2022, Runtime analysis of competitive co-evolutionary algorithms for maximin optimisation of a bilinear function. in JE Fieldsend (ed.), GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO: Genetic and Evolutionary Computation Conference, Association for Computing Machinery (ACM), New York, pp. 1408–1416, GECCO '22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, United States, 9/07/22. https://doi.org/10.1145/3512290.3528853

Lehre, PK & Qin, X 2022, Self-adaptation via multi-objectivisation: a theoretical study. in JE Fieldsend (ed.), GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery (ACM), New York, pp. 1417-1425, GECCO '22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, United States, 9/07/22. https://doi.org/10.1145/3512290.3528836

Qin, X & Lehre, PK 2022, Self-adaptation via multi-objectivisation: an empirical study. in G Rudolph, AV Kononova, H Aguirre, P Kerschke, G Ochoa & T Tušar (eds), Parallel Problem Solving from Nature – PPSN XVII: 17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part I. 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13398 LNCS, Springer, pp. 308–323, The seventeenth International Conference on Parallel Problem Solving from Nature, Dortmund, Germany, 10/09/22. https://doi.org/10.1007/978-3-031-14714-2_22

Lehre, PK & Qin, X 2021, More precise runtime analyses of non-elitist EAs in uncertain environments. in F Chicano (ed.), GECCO '21: Proceedings of the 2021 Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference (GECCO), Association for Computing Machinery (ACM), New York, pp. 1160-1168, Genetic and Evolutionary Computation Conference, 10/07/21. https://doi.org/10.1145/3449639.3459312

Dang, D-C, Eremeev, A & Lehre, PK 2021, Non-elitist evolutionary algorithms excel in fitness landscapes with sparse deceptive regions and dense valleys. in F Chicano (ed.), GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference (GECCO), Association for Computing Machinery (ACM), New York, pp. 1133–1141, 2021 Genetic and Evolutionary Computation Conference, GECCO 2021, Virtual, Online, France, 10/07/21. https://doi.org/10.1145/3449639.3459398

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

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