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

Conference contribution

Lehre, PK, Fajardo, MH, Toutouh, J, Hemberg, E & O'Reilly, U-M 2023, Analysis of a Pairwise Dominance Coevolutionary Algorithm And DefendIt. in GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO: Genetic and Evolutionary Computation Conference, Association for Computing Machinery (ACM), pp. 1027-1035, GECCO '23: Genetic and Evolutionary Computation Conference, Lisbon, Portugal, 15/07/23. https://doi.org/10.1145/3583131.3590411

Hevia Fajardo, M & Lehre, PK 2023, How Fitness Aggregation Methods Affect the Performance of Competitive CoEAs on Bilinear Problems. in GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO: Genetic and Evolutionary Computation Conference, Association for Computing Machinery (ACM), pp. 1593-1601, GECCO '23: Genetic and Evolutionary Computation Conference, Lisbon, Portugal, 15/07/23. https://doi.org/10.1145/3583131.3590506

Liang, Z, Li, M & Lehre, PK 2023, Non-Elitist Evolutionary Multi-Objective Optimisation: Proof-of-Principle Results. in GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation. GECCO: Genetic and Evolutionary Computation Conference, Association for Computing Machinery (ACM), pp. 383-386, GECCO '23: Genetic and Evolutionary Computation Conference, Lisbon, Portugal, 15/07/23. https://doi.org/10.1145/3583133.3590646

Hevia Fajardo, M, Lehre, PK & Lin, S 2023, Runtime Analysis of a Co-Evolutionary Algorithm: Overcoming Negative Drift in Maximin-Optimisation. in FOGA '23: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. FOGA: Foundations of Genetic Algorithms, Association for Computing Machinery (ACM), pp. 73–83, Foundations of Genetic Algorithms XVII, Potsdam, Germany, 30/08/23. https://doi.org/10.1145/3594805.3607132

Hevia Fajardo, M, Lehre, PK & Lin, S 2023, Runtime Analysis of a Co-Evolutionary Algorithm: Overcoming Negative Drift in Maximin-Optimisation. in GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation. GECCO: Genetic and Evolutionary Computation Conference, Association for Computing Machinery (ACM), pp. 819–822, GECCO '23: Genetic and Evolutionary Computation Conference, Lisbon, Portugal, 15/07/23. https://doi.org/10.1145/3583133.3590701

Lehre, PK & Qin, X 2023, Self-adaptation Can Help Evolutionary Algorithms Track Dynamic Optima. in GECCO ’23: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO: Genetic and Evolutionary Computation Conference, Association for Computing Machinery (ACM), pp. 1619–1627, GECCO '23: Genetic and Evolutionary Computation Conference, Lisbon, Portugal, 15/07/23. https://doi.org/10.1145/3583131.3590494

Lehre, PK & Qin, X 2023, Self-adaptation Can Improve the Noise-tolerance of Evolutionary Algorithms. in FOGA '23: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. Association for Computing Machinery (ACM), pp. 105-116, Foundations of Genetic Algorithms XVII, Potsdam, Germany, 30/08/23. https://doi.org/10.1145/3594805.3607128

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. GECCO: 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

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