Abhiroop Ghosh

Computational Theory and Mathematics · 2017

A researcher in the Science corpus: 1 paper; trusts 10 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 36 citations into it; #6,950 of 12,182 by trust here, #7,851 by citations
• Artificial Intelligence — 34 citations into it; #20,877 of 35,345 by trust here, #27,551 by citations
• Management Science and Operations Research — 13 citations into it

Whom Abhiroop Ghosh trusts overall

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

1. Kalyanmoy Deb
2. Qingfu Zhang
3. Eckart Zitzler
4. Hui Li
5. Himanshu Jain
6. Ke Li

Who trusts Abhiroop Ghosh overall

1. Yong Wang
2. Ryoji Tanabe
3. Hisao Ishibuchi
4. Zhi-Zhong Liu
5. Zhongwei Ma

Papers from Abhiroop Ghosh's perspective

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

1. A fast and elitist multiobjective genetic algorithm: NSGA-II
2. MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition (IEEE Transactions on Evolutionary Computation)
3. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results (Evolutionary Computation)
4. A review of multiobjective test problems and a scalable test problem toolkit (IEEE Transactions on Evolutionary Computation)
5. SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization
6. Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II (IEEE Transactions on Evolutionary Computation)
7. 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)
8. Scalable multi-objective optimization test problems (Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600))
9. 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)
10. Decomposition of a Multiobjective Optimization Problem Into a Number of Simple Multiobjective Subproblems (IEEE Transactions on Evolutionary Computation)

Their papers

• A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition (2017) — 550 citations