2007 - Member of the National Academy of Engineering For contributions to the science and engineering innovations for electroceramics.
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.
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.
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.
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.
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.
Evolutionary programming made faster
Xin Yao;Yong Liu;Guangming Lin.
IEEE Transactions on Evolutionary Computation (1999)
Evolving artificial neural networks
Proceedings of the IEEE (1999)
Stochastic ranking for constrained evolutionary optimization
T.P. Runarsson;Xin Yao.
IEEE Transactions on Evolutionary Computation (2000)
Parallel Problem Solving from Nature - PPSN VIII
Xin Yao;Edmund K. Burke;José A. Lozano;Jim Smith.
arXiv: Neural and Evolutionary Computing (2004)
A new evolutionary system for evolving artificial neural networks
X. Yao;Y. Liu.
IEEE Transactions on Neural Networks (1997)
Diversity creation methods: a survey and categorisation
Gavin Brown;Jeremy L. Wyatt;Rachel Harris;Xin Yao.
Information Fusion (2004)
Benchmark Functions for the CEC'2008 Special Session and Competition on Large Scale Global Optimization
K. Tang;X. Yao;P. N. Suganthan;C. MacNish.
Large scale evolutionary optimization using cooperative coevolution
Zhenyu Yang;Ke Tang;Xin Yao.
Information Sciences (2008)
A review of evolutionary artificial neural networks
International Journal of Intelligent Systems (1993)
Ensemble learning via negative correlation
Y. Liu;X. Yao.
Neural Networks (1999)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: