World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
10455
World Ranking
6419
National Ranking
2862

Erik D. Goodman 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 Erik D. Goodman 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: 254 publications — 64th percentile

64% of scientists in this discipline score the same or lower.

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

Erik D. Goodman 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 Erik D. Goodman 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: 47 D-Index — 56th percentile

56% of scientists in this discipline score the same or lower.

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

Overview

Erik D. Goodman is affiliated with Michigan State University in the United States. Their research primarily focuses on fields within computer science and engineering, with a significant emphasis on artificial intelligence, computational theory and mathematics, and computer vision and pattern recognition. Additional topics of interest include plant science and management science and operations research.

Their work encompasses various specialized areas, including advanced multi-objective optimization algorithms, metaheuristic optimization algorithms research, evolutionary algorithms and applications, advanced neural network applications, machine learning and data classification, domain adaptation and few-shot learning, and optimal experimental design methods.

Goodman has contributed publications to multiple venues, reflecting a diverse range of research outputs. Frequent publication venues include:

  • ACM SIGEVOlution (5 publications)
  • IEEE Transactions on Evolutionary Computation (4 publications)
  • IEEE Transactions on Cybernetics (4 publications)
  • arXiv (Cornell University) (3 publications)
  • Knowledge-Based Systems (2 publications)

Selected recent papers illustrate the scope and topics of Goodman's research:

  • Multiobjective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification, 2020, IEEE Transactions on Evolutionary Computation
  • Neural Architecture Transfer, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • A New Many-Objective Evolutionary Algorithm Based on Generalized Pareto Dominance, 2021, IEEE Transactions on Cybernetics
  • A new adaptive decomposition-based evolutionary algorithm for multi- and many-objective optimization, 2022, Expert Systems with Applications
  • A Cooperative Evolutionary Framework Based on an Improved Version of Directed Weight Vectors for Constrained Multiobjective Optimization With Deceptive Constraints, 2020, IEEE Transactions on Cybernetics

Collaborations are evident through frequent co-authors, highlighting partnerships with:

  • Kalyanmoy Deb (30 collaborations)
  • Dhish Kumar Saxena (17 collaborations)
  • Sukrit Mittal (16 collaborations)
  • Lihong Xu (9 collaborations)
  • Zhichao Lu (7 collaborations)

Goodman has also contributed to book publications through Springer Nature, with works including:

  • Genetic Programming Theory and Practice XVII, 2020
  • Machine Learning Assisted Evolutionary Multi- and Many-Objective Optimization, 2024

Best Publications

  • Dimensionality reduction using genetic algorithms

    M.L. Raymer;W.F. Punch;E.D. Goodman;L.A. Kuhn

  • NSGA-Net: neural architecture search using multi-objective genetic algorithm

    Zhichao Lu;Ian Whalen;Vishnu Boddeti;Yashesh Dhebar

  • Push and pull search for solving constrained multi-objective optimization problems

    Zhun Fan;Wenji Li;Xinye Cai;Hui Li

  • Further Research on Feature Selection and Classification Using Genetic Algorithms

    William F. Punch;Erik D. Goodman;Min Pei;Lai Chia-Shun

  • An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions

    Zhun Fan;Wenji Li;Xinye Cai;Han Huang

  • Coarse-grain parallel genetic algorithms: categorization and new approach

    Shyh-Chang Lin;W.F. Punch;E.D. Goodman

  • Difficulty Adjustable and Scalable Constrained Multiobjective Test Problem Toolkit.

    Zhun Fan;Wenji Li;Xinye Cai;Hui Li

  • Predicting conserved water-mediated and polar ligand interactions in proteins using a K-nearest-neighbors genetic algorithm.

    Michael L. Raymer;Paul C. Sanschagrin;William F. Punch;Sridhar Venkataraman

  • Evolutionary Dynamic Multiobjective Optimization Assisted by a Support Vector Regression Predictor

    Leilei Cao;Lihong Xu;Erik D. Goodman;Chunteng Bao

  • Method and product for determining salient features for use in information searching

    William F. Punch;Marilyn R. Wulfekuhler;Erik D. Goodman

  • NSGANetV2: Evolutionary Multi-objective Surrogate-Assisted Neural Architecture Search

    Zhichao Lu;Kalyanmoy Deb;Erik D. Goodman;Wolfgang Banzhaf

  • Multiobjective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification

    Zhichao Lu;Ian Whalen;Yashesh Dhebar;Kalyanmoy Deb

  • A Genetic Algorithm Approach to Dynamic Job Shop Scheduling Problem.

    Shyh-Chang Lin;Erik D. Goodman;William F. Punch

  • A Standard GA Approach to Native Protein Conformation Prediction

    Arnold L. Patton;William F. Punch;Erik D. Goodman

  • Neural Architecture Transfer

    Zhichao Lu;Gautam Sreekumar;Erik Goodman;Wolfgang Banzhaf

  • Direct dimensional NC verification

    J. H. Oliver;E. D. Goodman

  • MOEA/D with angle-based constrained dominance principle for constrained multi-objective optimization problems

    Zhun Fan;Yi Fang;Wenji Li;Xinye Cai;Xinye Cai

  • Swarmed feature selection

    H.A. Firpi;E. Goodman

  • The Hierarchical Fair Competition (HFC) Framework for Sustainable Evolutionary Algorithms

    Jianjun Hu;Erik Goodman;Kisung Seo;Zhun Fan

  • Toward a unified and automated design methodology for multi-domain dynamic systems using bond graphs and genetic programming

    Kisung Seo;Zhun Fan;Jianjun Hu;Erik D. Goodman

  • The hierarchical fair competition (HFC) model for parallel evolutionary algorithms

    Jian Jun Hu;E.D. Goodman

  • Introduction to genetic algorithms

    Unknown

  • Difficulty Adjustable and Scalable Constrained Multi-objective Test Problem Toolkit

    Zhun Fan;Wenji Li;Xinye Cai;Hui Li

Frequent Co-Authors

Zhun Fan
Zhun Fan Shantou University
Kalyanmoy Deb
Kalyanmoy Deb Michigan State University
William F. Punch
William F. Punch Michigan State University
Wolfgang Banzhaf
Wolfgang Banzhaf Michigan State University
Qingfu Zhang
Qingfu Zhang City University of Hong Kong
Erik S. Runkle
Erik S. Runkle Michigan State University
Xiaobo Tan
Xiaobo Tan Michigan State University
Danwei Wang
Danwei Wang Nanyang Technological University
Mohsen Shahinpoor
Mohsen Shahinpoor University of Maine

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