Hui Wang

Artificial Intelligence · Information Sciences · 2011–2017

A researcher in the Science corpus: 3 papers; trusts 17 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

Whom Hui Wang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Salvador Garcia
  2. Jaka Demsar
  3. Francisco Herrera
  4. Xin-She Yang
  5. Hamid R. Tizhoosh
  6. Frans van den Bergh
  7. Xiaohui Hu
  8. Rui Mendes
  9. Jing J. Liang
  10. James Kennedy

Who trusts Hui Wang overall

  1. Dongshu Wang
  2. Yudong Zhang
  3. Konstantinos E. Parsopoulos
  4. Sedigheh Mahdavi
  5. Mohammad Reza Bonyadi
  6. Muhammad Rizwan Tanweer
  7. Dapei Tan
  8. Shuihua Wang
  9. Lei Liu
  10. Genlin Ji

Papers from Hui Wang's perspective

Ordered by how strongly the researchers Hui Wang trusts endorse each paper. Same corpus, a different order for every researcher.

  1. No free lunch theorems for optimization
  2. Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization
  3. A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization (J. Heuristics)
  4. Statistical Comparisons of Classifiers over Multiple Data Sets
  5. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions (IEEE Transactions on Evolutionary Computation)
  6. An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons
  7. A Cooperative approach to particle swarm optimization (IEEE Transactions on Evolutionary Computation)
  8. Population structure and particle swarm performance (Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600))
  9. A new optimizer using particle swarm theory
  10. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence

Their papers