D. Gong

Artificial Intelligence · 2010–2020

A researcher in the Science corpus: 6 papers; trusts 41 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 54 citations into it; #22,585 of 35,345 by trust here, #20,836 by citations
• Computational Theory and Mathematics — 45 citations into it; #8,751 of 12,182 by trust here, #4,696 by citations
• Electrical and Electronic Engineering — 24 citations into it; #9,991 of 16,539 by trust here, #10,714 by citations
• Computer Vision and Pattern Recognition — 8 citations into it; #17,799 of 21,575 by trust here, #20,271 by citations
• Evaluation & Benchmarks — 7 citations into it
• Control and Systems Engineering — 6 citations into it

Whom D. Gong trusts overall

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

1. Kalyanmoy Deb
2. Lingfeng Wang
3. Carlos Artemio Coello Coello
4. Mohammad Ali Abido
5. Saudi Arabia
6. Malabika Basu
7. James Kennedy
8. Eckart Zitzler
9. Samir Agrawal
10. Sanaz Mostaghim

Who trusts D. Gong overall

1. Abdolreza Hatamlou
2. Zhile Yang
3. Gwo-Ching Liao
4. Manh Duong Phung
5. Mehrdad Rostami
6. Shanu Verma
7. Kang Li
8. Taher Niknam

Papers from D. Gong's perspective

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

1. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results (Evolutionary Computation)
2. A fast and elitist multiobjective genetic algorithm: NSGA-II
3. Evolutionary Algorithms for Solving Multi-Objective Problems
4. Comparison of algorithms that select features for pattern classifiers (Pattern Recognition)
5. On measuring multiobjective evolutionary algorithm performance (Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512))
6. Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art
7. Feature selection based on rough sets and particle swarm optimization (Pattern Recognition Letters)
8. TWO NEW ROBUST GENETIC ALGORITHMS FOR THE FLOWSHOP SCHEDULING PROBLEM
9. Analytical solution for combined economic and emissions dispatch
10. Environmental/economic power dispatch using a fuzzified multi-objective particle swarm optimization algorithm

Their papers

• Environmental/economic power dispatch using a hybrid multi-objective optimization algorithm (2010) — 196 citations
• Binary differential evolution with self-learning for multi-objective feature selection (2020) — 399 citations
• Robot path planning in uncertain environment using multi-objective particle swarm optimization (2013) — 391 citations
• A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch (2012) — 295 citations
• Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification (2017) — 314 citations
• An improved NSGA-II algorithm for multi-objective lot-streaming flow shop scheduling problem (2014) — 78 citations