Hiroyuki Masuda

Computational Theory and Mathematics · 2015–2017

A researcher in the Science corpus: 2 papers; trusts 17 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 38 citations into it; #5,049 of 12,182 by trust here, #6,798 by citations
• Artificial Intelligence — 35 citations into it; #20,642 of 35,345 by trust here, #28,481 by citations
• Management Science and Operations Research — 11 citations into it

Whom Hiroyuki Masuda trusts overall

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

1. Kalyanmoy Deb
2. Eckart Zitzler
3. Himanshu Jain
4. Carlos Artemio Coello Coello
5. Hisao Ishibuchi
6. Lothar Thiele
7. Peter A. N. Bosman
8. Qingfu Zhang
9. Johannes Bader
10. Joshua D. Knowles

Who trusts Hiroyuki Masuda overall

1. Yanan Sun
2. Ye Tian
3. Gary G. Yen
4. Ryoji Tanabe

Papers from Hiroyuki Masuda's perspective

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

1. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results (Evolutionary Computation)
2. Scalable Test Problems for Evolutionary Multiobjective Optimization (Evolutionary Multiobjective Optimization)
3. A fast and elitist multiobjective genetic algorithm: NSGA-II
4. Performance assessment of multiobjective optimizers: an analysis and review (IEEE Transactions on Evolutionary Computation)
5. On metrics for comparing nondominated sets (Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600))
6. A review of multiobjective test problems and a scalable test problem toolkit (IEEE Transactions on Evolutionary Computation)
7. MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition (IEEE Transactions on Evolutionary Computation)
8. SPEA2: Improving the strength pareto evolutionary algorithm
9. HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization (Evolutionary Computation)
10. The balance between proximity and diversity in multiobjective evolutionary algorithms (IEEE Transactions on Evolutionary Computation)

Their papers

• Modified Distance Calculation in Generational Distance and Inverted Generational Distance (2015) — 615 citations
• Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes (2017) — 503 citations