World's Best Scientists 2026 revealed!
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Mengjie Zhang
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Computer Science
New Zealand
2026

D-Index & Metrics

Computer Science

D-Index
79
Citations
31473
World Ranking
1129
National Ranking
2

Mengjie Zhang 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 Mengjie Zhang 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: 909 publications — 99th percentile

99% of scientists in this discipline score the same or lower.

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

Mengjie Zhang 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 Mengjie Zhang 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: 79 D-Index — 92nd percentile

92% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2026 - Research.com Computer Science in New Zealand Leader Award
  • 2025 - Research.com Computer Science in New Zealand Leader Award
  • 2023 - Research.com Computer Science in New Zealand Leader Award
  • 2022 - Research.com Computer Science in New Zealand Leader Award
  • 2019 - IEEE Fellow For contributions to evolutionary learning and optimization methodologies
  • 2017 - Fellow of the Royal Society of New Zealand

Overview

Mengjie Zhang is affiliated with Victoria University of Wellington in New Zealand. Their research primarily spans the fields of Computer Science and Engineering, with a significant focus on subfields such as Artificial Intelligence, Industrial and Manufacturing Engineering, Computational Theory and Mathematics, Computer Vision and Pattern Recognition, and Molecular Biology.

The scientist's work covers various topics, including:

  • Metaheuristic Optimization Algorithms Research
  • Evolutionary Algorithms and Applications
  • Advanced Multi-Objective Optimization Algorithms
  • Scheduling and Optimization Algorithms
  • Machine Learning and Data Classification
  • Reinforcement Learning in Robotics
  • Advanced Neural Network Applications

Selected recent papers authored or coauthored by Mengjie Zhang include:

  • Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification, 2020, IEEE Transactions on Cybernetics
  • A Survey on Swarm Intelligence Approaches to Feature Selection in Data Mining, 2020, Swarm and Evolutionary Computation
  • Evolving Scheduling Heuristics via Genetic Programming With Feature Selection in Dynamic Flexible Job-Shop Scheduling, 2020, IEEE Transactions on Cybernetics
  • An Evolutionary Multitasking-Based Feature Selection Method for High-Dimensional Classification, 2020, IEEE Transactions on Cybernetics
  • Surrogate-Assisted Evolutionary Multitask Genetic Programming for Dynamic Flexible Job Shop Scheduling, 2021, IEEE Transactions on Evolutionary Computation

The scientist has published extensively in several venues, including:

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

Mengjie Zhang has collaborated frequently with coauthors such as Bing Xue, Yi Mei, Fangfang Zhang, Ying Bi, and Qi Chen.

The scientist has also contributed to book publications through Springer Nature, including titles:

  • Genetic Programming for Production Scheduling, 2021
  • Handbook of Evolutionary Machine Learning, 2023
  • Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances, 2022

Awards received include Fellow of the Royal Society of New Zealand in 2017 and IEEE Fellow in 2019, with the latter recognizing contributions to evolutionary learning and optimization methodologies.

Best Publications

  • A Survey on Evolutionary Computation Approaches to Feature Selection

    Bing Xue;Mengjie Zhang;Will N. Browne;Xin Yao

  • Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach

    Bing Xue;Mengjie Zhang;Will N. Browne

  • Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation

    Muhammad Ghifary;W. Bastiaan Kleijn;Mengjie Zhang;David Balduzzi

  • Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification

    Yanan Sun;Bing Xue;Mengjie Zhang;Gary G. Yen

  • Evolving Deep Convolutional Neural Networks for Image Classification

    Yanan Sun;Bing Xue;Mengjie Zhang;Gary G. Yen

  • Particle swarm optimisation for feature selection in classification

    Bing Xue;Mengjie Zhang;Will N. Browne

  • Domain Generalization for Object Recognition with Multi-task Autoencoders

    Muhammad Ghifary;W. Bastiaan Kleijn;Mengjie Zhang;David Balduzzi

  • Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization

    Muhammad Ghifary;David Balduzzi;W. Bastiaan Kleijn;Mengjie Zhang

  • Automated Design of Production Scheduling Heuristics: A Review

    Jurgen Branke;Su Nguyen;Christoph W. Pickardt;Mengjie Zhang

  • Domain Adaptive Neural Networks for Object Recognition

    Muhammad Ghifary;W. Bastiaan Kleijn;Mengjie Zhang

  • A Survey on Evolutionary Neural Architecture Search.

    Yuqiao Liu;Yanan Sun;Bing Xue;Mengjie Zhang

  • Differential evolution for filter feature selection based on information theory and feature ranking

    Emrah Hancer;Emrah Hancer;Bing Xue;Mengjie Zhang

  • A survey on swarm intelligence approaches to feature selection in data mining

    Bach Hoai Nguyen;Bing Xue;Mengjie Zhang

  • Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification

    Yu Xue;Bing Xue;Mengjie Zhang

  • Pareto front feature selection based on artificial bee colony optimization

    Emrah Hancer;Emrah Hancer;Bing Xue;Mengjie Zhang;Dervis Karaboga

  • Completely Automated CNN Architecture Design Based on Blocks

    Yanan Sun;Bing Xue;Mengjie Zhang;Gary G. Yen

  • Variable-Length Particle Swarm Optimization for Feature Selection on High-Dimensional Classification

    Binh Tran;Bing Xue;Mengjie Zhang

  • Evolving Diverse Ensembles Using Genetic Programming for Classification With Unbalanced Data

    U. Bhowan;M. Johnston;Mengjie Zhang;Xin Yao

  • Automatic Design of Scheduling Policies for Dynamic Multi-objective Job Shop Scheduling via Cooperative Coevolution Genetic Programming

    Su Nguyen;Mengjie Zhang;Mark Johnston;Kay Chen Tan

  • Genetic programming for production scheduling: a survey with a unified framework

    Su Nguyen;Yi Mei;Mengjie Zhang

  • Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor

    Yanan Sun;Handing Wang;Bing Xue;Yaochu Jin

  • Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction

    Rohitash Chandra;Mengjie Zhang

  • Practice and Theory of Blendshape Facial Models

    John P. Lewis;Ken Anjyo;Taehyun Rhee;Mengjie Zhang

  • A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem

    Su Nguyen;Mengjie Zhang;Mark Johnston;Kay Chen Tan

  • Evolving Scheduling Heuristics via Genetic Programming With Feature Selection in Dynamic Flexible Job-Shop Scheduling

    Fangfang Zhang;Yi Mei;Su Nguyen;Mengjie Zhang

Frequent Co-Authors

Bing Xue
Bing Xue Victoria University of Wellington
Yi Mei
Yi Mei Victoria University of Wellington
Su Nguyen
Su Nguyen RMIT University
Kay Chen Tan
Kay Chen Tan Hong Kong Polytechnic University
Gary G. Yen
Gary G. Yen Oklahoma State University
W. Bastiaan Kleijn
W. Bastiaan Kleijn Victoria University of Wellington
Xiaodong Li
Xiaodong Li University of Virginia
Brijesh Verma
Brijesh Verma Central Queensland University
Yuhui Shi
Yuhui Shi Southern University of Science and Technology
Winston K. G. Seah
Winston K. G. Seah Victoria University of Wellington

External Links

Personal website of Mengjie Zhang Google Scholar page

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