What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Programming language
Edward Tsang mainly investigates Mathematical optimization, Evolutionary algorithm, Constraint satisfaction, Guided Local Search and Constraint satisfaction problem.
Mathematical optimization is closely attributed to Algorithm in his research.
His Evolutionary algorithm research includes themes of Pareto distribution and Crossover.
Edward Tsang focuses mostly in the field of Constraint satisfaction, narrowing it down to matters related to Scheduling and, in some cases, Containerization, Project management and Knowledge-based configuration.
His studies deal with areas such as Iterated local search, Cutting stock problem and Information retrieval as well as Guided Local Search.
In his research on the topic of Hybrid algorithm, Operations research is strongly related with Artificial intelligence.
His most cited work include:
- Foundations of Constraint Satisfaction (1543 citations)
- Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model (331 citations)
- Guided local search and its application to the traveling salesman problem (309 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Mathematical optimization, Artificial intelligence, Genetic programming, Constraint satisfaction and Constraint satisfaction problem.
His work in Mathematical optimization addresses subjects such as Algorithm, which are connected to disciplines such as Heuristics.
His biological study spans a wide range of topics, including Space and Machine learning.
His Genetic programming research also works with subjects such as
- Decision tree which intersects with area such as Operations research,
- Financial market which intersects with area such as Econometrics, Trading strategy, Market microstructure, Time series and Cluster analysis.
His Constraint satisfaction research is multidisciplinary, incorporating perspectives in Genetic algorithm and Scheduling.
His studies in Constraint satisfaction problem integrate themes in fields like Theoretical computer science, Constraint programming and Backtracking.
He most often published in these fields:
- Mathematical optimization (33.06%)
- Artificial intelligence (19.59%)
- Genetic programming (18.78%)
What were the highlights of his more recent work (between 2011-2020)?
- Financial market (12.65%)
- Econometrics (8.57%)
- Genetic programming (18.78%)
In recent papers he was focusing on the following fields of study:
Edward Tsang focuses on Financial market, Econometrics, Genetic programming, Artificial intelligence and Machine learning.
His Genetic programming research incorporates elements of Decision tree, Arbitrage, Financial modeling and Decision rule.
His Machine learning study combines topics in areas such as Space and Local search, Guided Local Search, Metaheuristic.
His Guided Local Search research is included under the broader classification of Mathematical optimization.
Edward Tsang combines subjects such as Rate-monotonic scheduling, Dynamic priority scheduling and Fair-share scheduling with his study of Mathematical optimization.
His study in Algorithm is interdisciplinary in nature, drawing from both Constraint satisfaction and Scheduling.
Between 2011 and 2020, his most popular works were:
- Novel constraints satisfaction models for optimization problems in container terminals (51 citations)
- Multiobjective genetic programming for maximizing ROC performance (31 citations)
- A genetic type-2 fuzzy logic based system for the generation of summarised linguistic predictive models for financial applications (26 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Algorithm
Edward Tsang mainly investigates Genetic programming, Econometrics, Artificial intelligence, Time series and Mathematical optimization.
His Genetic programming research is multidisciplinary, relying on both Decision tree, Arbitrage, Financial modeling and Decision rule.
His Decision tree research incorporates themes from Evolutionary algorithm and Receiver operating characteristic.
His Artificial intelligence research focuses on subjects like Machine learning, which are linked to Space, Constant, Heuristics, Complex system and Computational finance.
Edward Tsang incorporates Mathematical optimization and Function in his studies.
Edward Tsang interconnects Dynamic network analysis, Containerization and Constraint satisfaction problem in the investigation of issues within Optimization problem.
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