Jiajie Mo

Artificial Intelligence · 2017

A researcher in the Science corpus: 1 paper; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Jiajie Mo trusts

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

  1. Eckart Zitzler (in Computational Theory and Mathematics)
  2. Qingfu Zhang (in Artificial Intelligence)
  3. Carlos Artemio Coello Coello (in Computational Theory and Mathematics)
  4. Hui Li (in Computational Theory and Mathematics)
  5. Xin Yao (in Artificial Intelligence)
  6. Thomas Philip Runarsson (in Artificial Intelligence)
  7. Jianjun Li (in Management Science and Operations Research)
  8. Kalyanmoy Deb (in Computational Theory and Mathematics)

Papers from Jiajie Mo's perspective

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

  1. Stochastic ranking for constrained evolutionary optimization (IEEE Transactions on Evolutionary Computation)
  2. Indicator-Based Selection in Multiobjective Search (Parallel Problem Solving from Nature)
  3. A review of multiobjective test problems and a scalable test problem toolkit (IEEE Transactions on Evolutionary Computation)
  4. The balance between proximity and diversity in multiobjective evolutionary algorithms (IEEE Transactions on Evolutionary Computation)
  5. MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition (IEEE Transactions on Evolutionary Computation)
  6. A fast and elitist multiobjective genetic algorithm: NSGA-II
  7. Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II (IEEE Transactions on Evolutionary Computation)
  8. SPEA2: Improving the strength pareto evolutionary algorithm
  9. An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints (IEEE Transactions on Evolutionary Computation)
  10. THEORETICAL AND NUMERICAL CONSTRAINT-HANDLING TECHNIQUES USED WITH EVOLUTIONARY ALGORITHMS: A SURVEY OF THE STATE OF THE ART

Their papers