Zhongwei Ma
Artificial Intelligence · 2019
A researcher in the Science corpus: 1 paper; trusts 21 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 19 citations into it; #26,784 of 35,345 by trust here, #33,294 by citations
- Computational Theory and Mathematics — 19 citations into it; #9,240 of 12,182 by trust here, #10,901 by citations
Whom Zhongwei Ma trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kalyanmoy Deb
- Qingfu Zhang
- Peter A. N. Bosman
- Tetsuyuki Takahama
- Yonas G. Woldesenbet
- Hui Li
- Anupam Trivedi
- Thomas Philip Runarsson
- Dirk Thierens
- Setsuko Sakai
Who trusts Zhongwei Ma overall
Papers from Zhongwei Ma's perspective
Ordered by how strongly the researchers Zhongwei Ma trusts endorse each paper. Same corpus, a different order for every researcher.
- A fast and elitist multiobjective genetic algorithm: NSGA-II
- A review of multiobjective test problems and a scalable test problem toolkit (IEEE Transactions on Evolutionary Computation)
- MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition (IEEE Transactions on Evolutionary Computation)
- Stochastic ranking for constrained evolutionary optimization (IEEE Transactions on Evolutionary Computation)
- The balance between proximity and diversity in multiobjective evolutionary algorithms (IEEE Transactions on Evolutionary Computation)
- An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach (IEEE Transactions on Evolutionary Computation)
- On the Evolutionary Optimization of Many Conflicting Objectives (IEEE Transactions on Evolutionary Computation)
- Constraint Handling in Multiobjective Evolutionary Optimization (IEEE Transactions on Evolutionary Computation)
- Indicator-Based Selection in Multiobjective Search (Parallel Problem Solving from Nature)
- An Efficient Constraint Handling Method for Genetic Algorithms