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D-Index & Metrics

Computer Science

D-Index
47
Citations
10455
World Ranking
6418
National Ranking
2861

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

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
Edward J. Rothwell
Edward J. Rothwell Michigan State University
Danwei Wang
Danwei Wang Nanyang Technological University
Mohsen Shahinpoor
Mohsen Shahinpoor University of Maine

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