World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
65
Citations
25450
World Ranking
2403
National Ranking
134

Joshua Knowles 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 Joshua Knowles 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: 196 publications — 45th percentile

45% of scientists in this discipline score the same or lower.

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

Joshua Knowles 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 Joshua Knowles 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: 65 D-Index — 83rd percentile

83% of scientists in this discipline score the same or lower.

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

Overview

Joshua Knowles is affiliated with the University of Manchester in the United Kingdom. Their research primarily focuses on Computer Science, with significant contributions to Engineering. Within these fields, Knowles has specialized in Artificial Intelligence, Computational Theory and Mathematics, Industrial and Manufacturing Engineering, Control and Systems Engineering, and Computer Networks and Communications.

Knowles's work covers several main topics, including Advanced Multi-Objective Optimization Algorithms, Metaheuristic Optimization Algorithms Research, Evolutionary Algorithms and Applications, Process Optimization and Integration, Machine Learning and Algorithms, Evolutionary Game Theory and Cooperation, and Complex Systems and Time Series Analysis.

Their recent publications include:

  • Evolutionary Multiobjective Optimization (EMO), 2024, Proceedings of the Genetic and Evolutionary Computation Conference Companion
  • On Benchmarking Interactive Evolutionary Multiobjective Algorithms, 2023, IEEE Transactions on Evolutionary Computation
  • Deep Optimisation: Transitioning the Scale of Evolutionary Search by Inducing and Searching in Deep Representations, 2022, SN Computer Science
  • Heterogeneous Objectives: State-of-the-Art and Future Research, 2021, arXiv (Cornell University)
  • Realistic utility functions prove difficult for state-of-the-art interactive multiobjective optimization algorithms, 2021, Proceedings of the Genetic and Evolutionary Computation Conference

Joshua Knowles has frequently collaborated with authors such as Richard Allmendinger, Manuel López-Ibáñez, Seyed Mahdi Shavarani, Ștefan Pricopie, and Clyde Fare.

Their work has been published in various venues, reflecting a consistent presence in specific conferences and journals. These venues include:

  • Proceedings of the Genetic and Evolutionary Computation Conference Companion
  • arXiv (Cornell University)
  • Proceedings of the Genetic and Evolutionary Computation Conference
  • SN Computer Science
  • IEEE Transactions on Evolutionary Computation

Best Publications

  • Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy

    Joshua D. Knowles;David W. Corne

  • The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation

    J. Knowles;D. Corne

  • A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers

    Joshua Knowles;Lothar Thiele;Eckart Zitzler

  • ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems

    J. Knowles

  • The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation

    David Corne;Joshua D. Knowles;Martin J. Oates

  • PESA-II: region-based selection in evolutionary multiobjective optimization

    David W. Corne;Nick R. Jerram;Joshua D. Knowles;Martin J. Oates

  • Computational cluster validation in post-genomic data analysis

    Julia Handl;Joshua Knowles;Douglas B. Kell

  • An Evolutionary Approach to Multiobjective Clustering

    J. Handl;J. Knowles

  • On metrics for comparing nondominated sets

    J. Knowles;D. Corne

  • M-PAES: a memetic algorithm for multiobjective optimization

    J.D. Knowles;D.W. Corne

  • Reducing Local Optima in Single-Objective Problems by Multi-objectivization

    Joshua D. Knowles;Richard A. Watson;David Corne

  • A MAX-MIN Ant System for the University Course Timetabling Problem

    Krzysztof Socha;Joshua Knowles;Michael Sampels

  • Properties of an adaptive archiving algorithm for storing nondominated vectors

    J. Knowles;D. Corne

  • Quality Assessment of Pareto Set Approximations

    Eckart Zitzler;Joshua Knowles;Lothar Thiele

  • Techniques for highly multiobjective optimisation: some nondominated points are better than others

    David W. Corne;Joshua D. Knowles

  • Multiobjective Optimization in Bioinformatics and Computational Biology

    Julia Handl;Douglas B. Kell;Joshua Knowles

  • A comparison of the performance of different metaheuristics on the timetabling problem

    Olivia Rossi-Doria;Michael Sampels;Mauro Birattari;Marco Chiarandini

  • Local-Search and Hybrid Evolutionary Algorithms for Pareto Optimization

    Joshua Knowles

  • Ant-Based Clustering and Topographic Mapping

    J. Handl;J. Knowles;M. Dorigo

  • Fifty years of pulsar candidate selection: from simple filters to a new principled real-time classification approach

    Robert Lyon;Benjamin Stappers;Sally Cooper;John Martin Brooke

  • Supplementary material to computational cluster validation in post-genomic data analysis

    Julia Handl;Joshua Knowles;Douglas B. Kell

Frequent Co-Authors

David Corne
David Corne Heriot-Watt University
Douglas B. Kell
Douglas B. Kell University of Liverpool
Warwick B. Dunn
Warwick B. Dunn University of Liverpool
Kalyanmoy Deb
Kalyanmoy Deb Michigan State University
Marco Dorigo
Marco Dorigo Université Libre de Bruxelles
Royston Goodacre
Royston Goodacre University of Liverpool
David Broadhurst
David Broadhurst University of Alberta
David C. Wedge
David C. Wedge University of Manchester
Philip J. R. Day
Philip J. R. Day University of Manchester
Salvatore Greco
Salvatore Greco University of Portsmouth

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