Zhijian Wu

Artificial Intelligence · 2011

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

Whom Zhijian Wu trusts overall

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

  1. Salvador Garcia
  2. Hamid R. Tizhoosh
  3. Jaka Demsar
  4. Frans van den Bergh
  5. Shahryar Rahnamayan
  6. Francisco Herrera
  7. Rui Mendes
  8. Xiaohui Hu
  9. Jing J. Liang

Who trusts Zhijian Wu overall

  1. Konstantinos E. Parsopoulos
  2. Dongshu Wang
  3. Yudong Zhang

Papers from Zhijian Wu's perspective

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

  1. 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)
  2. Statistical Comparisons of Classifiers over Multiple Data Sets
  3. An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons
  4. Opposition-Based Differential Evolution (IEEE Transactions on Evolutionary Computation)
  5. Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power
  6. Opposition-Based Learning: A New Scheme for Machine Intelligence (International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06))
  7. A Cooperative approach to particle swarm optimization (IEEE Transactions on Evolutionary Computation)
  8. The fully informed particle swarm: simpler, maybe better (IEEE Transactions on Evolutionary Computation)
  9. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions (IEEE Transactions on Evolutionary Computation)
  10. A study of particle swarm optimization particle trajectories (Information Sciences)

Their papers