What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Zexuan Zhu focuses on Artificial intelligence, Data mining, Memetic algorithm, Feature selection and Machine learning.
His work on Artificial intelligence is being expanded to include thematically relevant topics such as Pattern recognition.
His Data mining research is multidisciplinary, incorporating perspectives in Computer security, Android malware, Malware and Rotation forest.
Mathematical optimization, Algorithm and Local search are inherently bound to his Memetic algorithm studies.
The study incorporates disciplines such as Feature extraction and Feature in addition to Feature selection.
Zexuan Zhu works mostly in the field of Machine learning, limiting it down to topics relating to Classifier and, in certain cases, Markov chain.
His most cited work include:
- Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework (343 citations)
- A fast pruned-extreme learning machine for classification problem (276 citations)
- Markov blanket-embedded genetic algorithm for gene selection (237 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Mathematical optimization, Evolutionary algorithm, Machine learning and Memetic algorithm.
His studies deal with areas such as Data mining and Pattern recognition as well as Artificial intelligence.
His work carried out in the field of Mathematical optimization brings together such families of science as Convergence and Benchmark.
Zexuan Zhu combines subjects such as Evolutionary computation, Pareto principle, Knowledge transfer and Crossover with his study of Evolutionary algorithm.
His Transfer of learning study in the realm of Machine learning connects with subjects such as Complex network and Empirical research.
His Feature selection research incorporates elements of Feature and Markov chain.
He most often published in these fields:
- Artificial intelligence (37.50%)
- Mathematical optimization (34.03%)
- Evolutionary algorithm (27.78%)
What were the highlights of his more recent work (between 2019-2021)?
- Evolutionary algorithm (27.78%)
- Mathematical optimization (34.03%)
- Evolutionary computation (15.28%)
In recent papers he was focusing on the following fields of study:
Zexuan Zhu spends much of his time researching Evolutionary algorithm, Mathematical optimization, Evolutionary computation, Optimization problem and Benchmark.
Artificial intelligence and Machine learning are inextricably linked to his Evolutionary algorithm research.
His study in the field of Feature and Least squares support vector machine is also linked to topics like Terahertz radiation, Potential toxicity and Graph.
The various areas that Zexuan Zhu examines in his Mathematical optimization study include Space, Selection and Crossover.
His Evolutionary computation research is multidisciplinary, relying on both Exploit and Biological network.
He works mostly in the field of Benchmark, limiting it down to concerns involving Ecological selection and, occasionally, Boundary.
Between 2019 and 2021, his most popular works were:
- A Survey of Weight Vector Adjustment Methods for Decomposition-Based Multiobjective Evolutionary Algorithms (11 citations)
- Toward Adaptive Knowledge Transfer in Multifactorial Evolutionary Computation (10 citations)
- Solving Generalized Vehicle Routing Problem With Occasional Drivers via Evolutionary Multitasking. (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His scientific interests lie mostly in Mathematical optimization, Evolutionary algorithm, Human multitasking, Evolutionary computation and Benchmark.
His study in Mathematical optimization is interdisciplinary in nature, drawing from both Knowledge transfer and Crossover.
Zexuan Zhu has included themes like Multi-objective optimization and Pareto principle in his Evolutionary computation study.
The Multi-objective optimization study combines topics in areas such as Space, IEEE Congress on Evolutionary Computation, Cluster analysis and Pruning.
His Benchmark research is multidisciplinary, incorporating elements of Decision variables and Simplex.
The concepts of his Optimization problem study are interwoven with issues in Local optimum and Rate of convergence.
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