What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer network
Jing Liu mainly investigates Evolutionary algorithm, Evolutionary computation, Mathematical optimization, Artificial intelligence and Theoretical computer science.
His Evolutionary algorithm study integrates concerns from other disciplines, such as Genetic algorithm, Algorithm design, Representation and Benchmark.
His research investigates the link between Genetic algorithm and topics such as Multi-agent system that cross with problems in Global optimum.
His study focuses on the intersection of Mathematical optimization and fields such as Job shop scheduling with connections in the field of Graph coloring and Constraint satisfaction problem.
His Artificial intelligence study frequently draws connections between adjacent fields such as Machine learning.
Jing Liu has researched Theoretical computer science in several fields, including Memetic algorithm, Computational complexity theory and Time complexity.
His most cited work include:
- A multiagent genetic algorithm for global numerical optimization (329 citations)
- Kernel Sparse Representation-Based Classifier (270 citations)
- Advances in Computational Intelligence (159 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Evolutionary algorithm, Artificial intelligence, Mathematical optimization, Machine learning and Benchmark.
His Evolutionary algorithm study combines topics from a wide range of disciplines, such as Evolutionary computation, Representation, Theoretical computer science and Complex network.
His Theoretical computer science research is multidisciplinary, incorporating elements of Node and Graph coloring.
His work on Pattern recognition expands to the thematically related Artificial intelligence.
As a member of one scientific family, Jing Liu mostly works in the field of Mathematical optimization, focusing on Multi-agent system and, on occasion, Time complexity.
Jing Liu interconnects Computational intelligence and Constraint satisfaction problem in the investigation of issues within Benchmark.
He most often published in these fields:
- Evolutionary algorithm (36.41%)
- Artificial intelligence (36.41%)
- Mathematical optimization (30.88%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (36.41%)
- Evolutionary algorithm (36.41%)
- Machine learning (20.28%)
In recent papers he was focusing on the following fields of study:
Jing Liu mostly deals with Artificial intelligence, Evolutionary algorithm, Machine learning, Complex network and Fuzzy cognitive map.
His Artificial intelligence research focuses on Pattern recognition and how it relates to Facial expression.
His Evolutionary algorithm research is within the category of Mathematical optimization.
His Mathematical optimization study incorporates themes from Sorting and Dimensionality reduction.
His Machine learning research is multidisciplinary, incorporating perspectives in Estimator and Effective algorithm.
His work deals with themes such as Theoretical computer science, Graph embedding, Fitness landscape, Community structure and Robustness, which intersect with Complex network.
Between 2018 and 2021, his most popular works were:
- A Deep One-Class Neural Network for Anomalous Event Detection in Complex Scenes (18 citations)
- Ensemble multi-objective evolutionary algorithm for gene regulatory network reconstruction based on fuzzy cognitive maps (18 citations)
- Designing comprehensively robust networks against intentional attacks and cascading failures (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Jing Liu focuses on Artificial intelligence, Complex network, Machine learning, Fuzzy cognitive map and Memetic algorithm.
His work in the fields of Artificial intelligence, such as Artificial neural network, Feature extraction and Benchmark, overlaps with other areas such as Dynamical systems theory.
His work investigates the relationship between Benchmark and topics such as Modality that intersect with problems in Similarity.
The study incorporates disciplines such as Evolutionary algorithm, Evolutionary computation, Distributed computing and Robustness in addition to Complex network.
His biological study spans a wide range of topics, including Theoretical computer science, Fuzzy set, Multi-objective optimization, Node and Focus.
The concepts of his Memetic algorithm study are interwoven with issues in Computational complexity theory, Data mining and Maximization.
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