What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Xin Yao focuses on Artificial intelligence, Mathematical optimization, Evolutionary algorithm, Evolutionary computation and Machine learning.
The study incorporates disciplines such as Algorithm design, Process and Pattern recognition in addition to Artificial intelligence.
He has researched Mathematical optimization in several fields, including Algorithm, Convergence and Benchmark.
His work carried out in the field of Evolutionary algorithm brings together such families of science as Time complexity, Genetic algorithm, Optimization problem and Metaheuristic.
His Evolutionary computation research is multidisciplinary, incorporating perspectives in Memetic algorithm and Selection.
His studies deal with areas such as Field, Data mining and Diversity as well as Machine learning.
His most cited work include:
- Evolutionary programming made faster (2693 citations)
- Evolving artificial neural networks (2491 citations)
- Stochastic ranking for constrained evolutionary optimization (1297 citations)
What are the main themes of his work throughout his whole career to date?
Xin Yao mainly focuses on Mathematical optimization, Artificial intelligence, Evolutionary algorithm, Evolutionary computation and Machine learning.
His studies in Mathematical optimization integrate themes in fields like Algorithm and Benchmark.
The study of Benchmark is intertwined with the study of Set in a number of ways.
Xin Yao has included themes like Data mining and Pattern recognition in his Artificial intelligence study.
His Evolutionary algorithm research integrates issues from Time complexity, Theoretical computer science and Crossover.
His study in Evolutionary computation focuses on Human-based evolutionary computation, Interactive evolutionary computation and Evolution strategy.
He most often published in these fields:
- Mathematical optimization (29.40%)
- Artificial intelligence (27.24%)
- Evolutionary algorithm (26.21%)
What were the highlights of his more recent work (between 2016-2021)?
- Mathematical optimization (29.40%)
- Evolutionary algorithm (26.21%)
- Artificial intelligence (27.24%)
In recent papers he was focusing on the following fields of study:
Mathematical optimization, Evolutionary algorithm, Artificial intelligence, Optimization problem and Benchmark are his primary areas of study.
Xin Yao interconnects Set and Solution set in the investigation of issues within Mathematical optimization.
The Solution set study combines topics in areas such as Algorithm and Pareto principle.
His Evolutionary algorithm research is multidisciplinary, relying on both Theoretical computer science, Computational intelligence, Selection and Crossover.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning, Task analysis and Pattern recognition.
His research in Benchmark intersects with topics in Linear programming and Reinforcement learning.
Between 2016 and 2021, his most popular works were:
- A benchmark test suite for evolutionary many-objective optimization (110 citations)
- A benchmark test suite for evolutionary many-objective optimization (110 citations)
- DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization (107 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His main research concerns Mathematical optimization, Evolutionary algorithm, Multi-objective optimization, Optimization problem and Artificial intelligence.
His Mathematical optimization study combines topics from a wide range of disciplines, such as Resource allocation, Decomposition, Constraint and Benchmark.
Xin Yao interconnects Theoretical computer science, Selection, Evolutionary computation, Fitness landscape and Crossover in the investigation of issues within Evolutionary algorithm.
His research in Evolutionary computation intersects with topics in Local search and Metaheuristic.
The study incorporates disciplines such as Test suite and Divide and conquer algorithms in addition to Optimization problem.
His studies deal with areas such as Data modeling, Generator, Task analysis, Machine learning and Pattern recognition as well as Artificial intelligence.
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