World's Best Scientists 2026 revealed!
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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

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
Kay Chen Tan
Kay Chen Tan Hong Kong Polytechnic University
W. Bastiaan Kleijn
W. Bastiaan Kleijn Victoria University of Wellington
Xiaodong Li
Xiaodong Li University of Virginia
Gary G. Yen
Gary G. Yen Oklahoma State University
Brijesh Verma
Brijesh Verma Central Queensland University
Yuhui Shi
Yuhui Shi Southern University of Science and Technology
Hisao Ishibuchi
Hisao Ishibuchi Southern University of Science and Technology
Winston K. G. Seah
Winston K. G. Seah Victoria University of Wellington

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