What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Mathematical optimization
Gai-Ge Wang mainly focuses on Metaheuristic, Mathematical optimization, Artificial intelligence, Algorithm and Optimization problem.
Gai-Ge Wang works mostly in the field of Metaheuristic, limiting it down to topics relating to Evolutionary computation and, in certain cases, Statistics.
His study in the fields of Krill herd algorithm and Meta-optimization under the domain of Mathematical optimization overlaps with other disciplines such as Krill.
His Artificial intelligence study which covers Swarm intelligence that intersects with Search algorithm, Global optimization problem and Metaheuristic optimization.
Gai-Ge Wang does research in Algorithm, focusing on Cuckoo search specifically.
His work deals with themes such as Genetic algorithm, Harmony search, Global optimization and Benchmark, which intersect with Optimization problem.
His most cited work include:
- Chaotic Krill Herd algorithm (317 citations)
- Monarch butterfly optimization (291 citations)
- Monarch butterfly optimization (291 citations)
What are the main themes of his work throughout his whole career to date?
Mathematical optimization, Algorithm, Metaheuristic, Optimization problem and Benchmark are his primary areas of study.
His study on Knapsack problem, Krill herd algorithm and Local search is often connected to Krill as part of broader study in Mathematical optimization.
His work in Algorithm tackles topics such as Harmony search which are related to areas like Optimization methods.
His Metaheuristic study combines topics in areas such as Evolutionary computation, Swarm intelligence, Continuous optimization and Travelling salesman problem.
The concepts of his Swarm intelligence study are interwoven with issues in Crossover and Artificial intelligence.
His Benchmark study incorporates themes from Global optimization, Biogeography-based optimization and Metaheuristic algorithms.
He most often published in these fields:
- Mathematical optimization (86.99%)
- Algorithm (43.90%)
- Metaheuristic (39.02%)
What were the highlights of his more recent work (between 2019-2021)?
- Algorithm (43.90%)
- Mathematical optimization (86.99%)
- Optimization problem (26.02%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Algorithm, Mathematical optimization, Optimization problem, Cuckoo search and Evolutionary algorithm.
His Algorithm research integrates issues from Fuzzy set, Fuzzy logic and Local area network.
His study connects Benchmark and Mathematical optimization.
His study on Benchmark also encompasses disciplines like
- Selection and related Metaheuristic algorithms,
- Swarm intelligence, which have a strong connection to Metaheuristic.
His studies deal with areas such as Travelling salesman problem, Floorplan and Topology as well as Optimization problem.
The various areas that Gai-Ge Wang examines in his Evolutionary algorithm study include Scale, Sorting and Crossover.
Between 2019 and 2021, his most popular works were:
- High Performance Computing for Cyber Physical Social Systems by Using Evolutionary Multi-Objective Optimization Algorithm (112 citations)
- Behavior of crossover operators in NSGA-III for large-scale optimization problems (57 citations)
- Interval Multiobjective Optimization With Memetic Algorithms (37 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Mathematical optimization
His primary scientific interests are in Mathematical optimization, Optimization problem, Evolutionary algorithm, Benchmark and Scale.
As part of his studies on Mathematical optimization, he often connects relevant subjects like Theoretical computer science.
He has researched Optimization problem in several fields, including Evolutionary computation and B-tree.
His Evolutionary algorithm research incorporates themes from Scale, Sorting and Crossover.
He works mostly in the field of Benchmark, limiting it down to concerns involving Selection and, occasionally, Metaheuristic algorithms and Swarm intelligence.
His Algorithm study combines topics from a wide range of disciplines, such as Fuzzy set and Evolution strategy.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
