Wudong Liu
Computational Theory and Mathematics · 2009–2010
A researcher in the Science corpus: 3 papers; trusts 11 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Computational Theory and Mathematics — 22 citations into it; #2,907 of 12,182 by trust here, #3,117 by citations
- Artificial Intelligence — 18 citations into it; #26,474 of 35,345 by trust here, #21,316 by citations
- Management Science and Operations Research — 5 citations into it; #8,738 of 11,946 by trust here, #5,316 by citations
- Statistics and Probability — 5 citations into it
Whom Wudong Liu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Qingfu Zhang
- Hui Li
- Kalyanmoy Deb
- Donald R. Jones
- Joshua D. Knowles
- Eckart Zitzler
- Kaisa Miettinen
- Yaochu Jin
- Carl E. Rasmussen
- Michael T. M. Emmerich
Who trusts Wudong Liu overall
- Yaochu Jin
- Handing Wang
- Ke Li
- Anupam Trivedi
- Ye Tian
- David Hadka
- Qingfu Zhang
- Xiaoliang Ma
- Xing-yi Zhang
- Yutao Qi
Papers from Wudong Liu's perspective
Ordered by how strongly the researchers Wudong Liu trusts endorse each paper. Same corpus, a different order for every researcher.
- Comparison of Multiobjective Evolutionary Algorithms: Empirical Results (Evolutionary Computation)
- Performance assessment of multiobjective optimizers: an analysis and review (IEEE Transactions on Evolutionary Computation)
- MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition (IEEE Transactions on Evolutionary Computation)
- Nonlinear multiobjective optimization
- A review of multiobjective test problems and a scalable test problem toolkit (IEEE Transactions on Evolutionary Computation)
- A fast and elitist multiobjective genetic algorithm: NSGA-II
- SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization
- A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code
- Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels (IEEE Transactions on Evolutionary Computation)
- A framework for evolutionary optimization with approximate fitness functions (IEEE Transactions on Evolutionary Computation)
Their papers
- The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances (2009) — 621 citations
- Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model (2010) — 701 citations
- Multiobjective optimization Test Instances for the CEC 2009 Special Session and Competition (2009) — 1,115 citations