World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
3616
World Ranking
12799
National Ranking
811

Per Kristian Lehre publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Per Kristian Lehre sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 137 publications — 21st percentile

21% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Per Kristian Lehre D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Per Kristian Lehre sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Per Kristian Lehre is affiliated with the University of Birmingham in the United Kingdom. Their research primarily focuses on areas within Computer Science, particularly Artificial Intelligence and Computational Theory and Mathematics. Lehre's work spans multiple subfields, including Management Science and Operations Research, Computer Networks and Communications, and Genetics.

Lehre's recent publication record illustrates a concentration on evolutionary algorithms and optimization techniques. Notable papers include:

  • Tail bounds on hitting times of randomized search heuristics using variable drift analysis, 2020, Combinatorics Probability Computing
  • Runtime analysis of competitive co-evolutionary algorithms for maximin optimisation of a bilinear function, 2022, Proceedings of the Genetic and Evolutionary Computation Conference

Frequent venues where Lehre publishes include the Proceedings of the Genetic and Evolutionary Computation Conference, where they have contributed extensively, as well as Algorithmica, arXiv (Cornell University), the Proceedings of the Genetic and Evolutionary Computation Conference Companion, and the Proceedings of the AAAI Conference on Artificial Intelligence.

The scientist's collaborations reflect ongoing partnerships with several researchers. Frequent co-authors include Xiaoyu Qin, Mario Alejandro Hevia Fajardo, Shishen Lin, Duc-Cuong Dang, and Anton V. Eremeev.

Research topics covered by Lehre include:

  • Metaheuristic Optimization Algorithms Research
  • Evolutionary Algorithms and Applications
  • Advanced Multi-Objective Optimization Algorithms
  • Reinforcement Learning in Robotics
  • Artificial Intelligence in Games
  • Game Theory and Applications
  • Optimization and Search Problems

In addition to the papers where Lehre is lead or sole author, they are associated with several highly cited works co-authored by Duc-Cuong Dang, such as:

  • Escaping Local Optima with Non-Elitist Evolutionary Algorithms, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • Non-elitist evolutionary algorithms excel in fitness landscapes with sparse deceptive regions and dense valleys, 2021, Proceedings of the Genetic and Evolutionary Computation Conference
  • Fast non-elitist evolutionary algorithms with power-law ranking selection, 2022, Proceedings of the Genetic and Evolutionary Computation Conference

Best Publications

  • Black-Box Search by Unbiased Variation

    Per Kristian Lehre;Carsten Witt

  • Black-box search by unbiased variation

    Per Kristian Lehre;Carsten Witt

  • Escaping Local Optima Using Crossover With Emergent Diversity

    Duc-Cuong Dang;Tobias Friedrich;Timo Kotzing;Martin S. Krejca

  • Level-Based Analysis of Genetic Algorithms and Other Search Processes

    Dogan Corus;Duc-Cuong Dang;Anton V. Eremeev;Per Kristian Lehre

  • Crossover can be constructive when computing unique input–output sequences

    Per Kristian Lehre;Xin Yao

  • Fitness-levels for non-elitist populations

    Per Kristian Lehre

  • Negative drift in populations

    Per Kristian Lehre

  • On the Impact of Mutation-Selection Balance on the Runtime of Evolutionary Algorithms

    P. K. Lehre;Xin Yao

  • Dynamic evolutionary optimisation: an analysis of frequency and magnitude of change

    Philipp Rohlfshagen;Per Kristian Lehre;Xin Yao

  • Self-adaptation of Mutation Rates in Non-elitist Populations

    Duc-Cuong Dang;Per Kristian Lehre

  • Unbiased Black-Box Complexity of Parallel Search

    Golnaz Badkobeh;Per Kristian Lehre;Dirk Sudholt

  • Runtime Analysis of Non-elitist Populations: From Classical Optimisation to Partial Information

    Duc-Cuong Dang;Per Kristian Lehre

  • On the impact of the mutation-selection balance on the runtime of evolutionary algorithms

    Per Kristian Lehre;Xin Yao

  • Escaping Local Optima with Diversity Mechanisms and Crossover

    Duc-Cuong Dang;Tobias Friedrich;Timo Kötzing;Martin S. Krejca

  • Faster black-box algorithms through higher arity operators

    Benjamin Doerr;Daniel Johannsen;Timo Kötzing;Per Kristian Lehre

  • Concentrated Hitting Times of Randomized Search Heuristics with Variable Drift

    Per Kristian Lehre;Carsten Witt

  • On the effect of populations in evolutionary multi-objective optimization

    Oliver Giel;Per Kristian Lehre

  • On the effect of populations in evolutionary multi-objective optimisation**

    Oliver Giel;Per Kristian Lehre

  • Crossover Can Be Constructive When Computing Unique Input Output Sequences

    Per Kristian Lehre;Xin Yao

  • When is an estimation of distribution algorithm better than an evolutionary algorithm

    Tianshi Chen;Per Kristian Lehre;Ke Tang;Xin Yao

  • A runtime analysis of simple hyper-heuristics: to mix or not to mix operators

    Per Kristian Lehre;Ender Özcan

  • Simplified Runtime Analysis of Estimation of Distribution Algorithms

    Duc-Cuong Dang;Per Kristian Lehre

Frequent Co-Authors

Xin Yao
Xin Yao Lingnan University
Dirk Sudholt
Dirk Sudholt University of Sheffield
Frank Neumann
Frank Neumann University of Adelaide
Carsten Witt
Carsten Witt Technical University of Denmark
Benjamin Doerr
Benjamin Doerr École Polytechnique
Tobias Friedrich
Tobias Friedrich Hasso Plattner Institute
Julian F. Miller
Julian F. Miller University of York
Thomas Jansen
Thomas Jansen Aberystwyth University
Mike Preuss
Mike Preuss Leiden University
Carlos M. Fonseca
Carlos M. Fonseca University of Coimbra

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