D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Engineering and Technology D-index 52 Citations 14,069 231 World Ranking 1339 National Ranking 151

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

His scientific interests lie mostly in Mathematical optimization, Evolutionary algorithm, Optimization problem, Evolutionary computation and Benchmark. In his study, Local search is inextricably linked to Arc routing, which falls within the broad field of Mathematical optimization. Evolutionary algorithm is a subfield of Machine learning that he tackles.

He interconnects Selection, Algorithm design, Estimation of distribution algorithm and Global optimization in the investigation of issues within Optimization problem. His studies in Evolutionary computation integrate themes in fields like Ranking, Metaheuristic and Robustness. His Benchmark research includes themes of Range, Cooperative coevolution, Continuous optimization and Decomposition.

His most cited work include:

  • Large scale evolutionary optimization using cooperative coevolution (712 citations)
  • Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale Global Optimization (539 citations)
  • Many-Objective Evolutionary Algorithms: A Survey (398 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of study are Mathematical optimization, Evolutionary algorithm, Artificial intelligence, Optimization problem and Machine learning. Ke Tang has researched Mathematical optimization in several fields, including Arc routing and Benchmark. Ke Tang combines subjects such as Multi-objective optimization, Selection, Genetic algorithm and Algorithm with his study of Evolutionary algorithm.

As part of the same scientific family, he usually focuses on Artificial intelligence, concentrating on Pattern recognition and intersecting with Object detection. The concepts of his Optimization problem study are interwoven with issues in Robust optimization, Global optimization, Linear programming, Cooperative coevolution and Robustness. His Machine learning study incorporates themes from Training set, Data mining and Empirical research.

He most often published in these fields:

  • Mathematical optimization (46.88%)
  • Evolutionary algorithm (33.98%)
  • Artificial intelligence (31.25%)

What were the highlights of his more recent work (between 2017-2021)?

  • Mathematical optimization (46.88%)
  • Evolutionary algorithm (33.98%)
  • Optimization problem (23.83%)

In recent papers he was focusing on the following fields of study:

Mathematical optimization, Evolutionary algorithm, Optimization problem, Theoretical computer science and Algorithm are his primary areas of study. The various areas that Ke Tang examines in his Mathematical optimization study include Range, Computational complexity theory, Selection and Benchmark. His Evolutionary algorithm study is associated with Artificial intelligence.

The Optimization problem study combines topics in areas such as Divide and conquer algorithms, Genetic algorithm, Linear programming, Optimization algorithm and Robustness. His study on Theoretical computer science also encompasses disciplines like

  • Travelling salesman problem that connect with fields like Heuristic,
  • Vehicle routing problem together with Crossover,
  • Solver which connect with Domain knowledge and Boolean satisfiability problem. His Algorithm research incorporates themes from Estimator and Pruning.

Between 2017 and 2021, his most popular works were:

  • A Survey on Cooperative Co-Evolutionary Algorithms (53 citations)
  • Concept Drift Adaptation by Exploiting Historical Knowledge (43 citations)
  • Turning High-Dimensional Optimization Into Computationally Expensive Optimization (39 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Algorithm

Ke Tang mainly focuses on Evolutionary algorithm, Mathematical optimization, Optimization problem, Algorithm and Artificial intelligence. His work on Divide and conquer algorithms expands to the thematically related Evolutionary algorithm. As part of one scientific family, Ke Tang deals mainly with the area of Mathematical optimization, narrowing it down to issues related to the Constraint, and often Matroid.

His Optimization problem study combines topics in areas such as Decision variables, Genetic algorithm and Benchmark. When carried out as part of a general Algorithm research project, his work on IEEE Congress on Evolutionary Computation and Differential evolution is frequently linked to work in Layer wise, therefore connecting diverse disciplines of study. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Structure and Multi-objective optimization, Machine learning.

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.

Best Publications

Benchmark Functions for the CEC'2008 Special Session and Competition on Large Scale Global Optimization

K. Tang;X. Yao;P. N. Suganthan;C. MacNish.
(2008)

875 Citations

Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale Global Optimization

Ke Tang;Xiaodong Li;P. N. Suganthan;Zhenyu Yang.
(2010)

857 Citations

Large scale evolutionary optimization using cooperative coevolution

Zhenyu Yang;Ke Tang;Xin Yao.
Information Sciences (2008)

835 Citations

Self-adaptive differential evolution with neighborhood search

Zhenyu Yang;Ke Tang;Xin Yao.
world congress on computational intelligence (2008)

405 Citations

Many-Objective Evolutionary Algorithms: A Survey

Bingdong Li;Jinlong Li;Ke Tang;Xin Yao.
ACM Computing Surveys (2015)

401 Citations

Multilevel cooperative coevolution for large scale optimization

Zhenyu Yang;Ke Tang;Xin Yao.
world congress on computational intelligence (2008)

328 Citations

Benchmark Functions for the CEC'2013 Special Session and Competition on Large-Scale Global Optimization

Xiaodong Li;Ke Tang;Mohammad N. Omidvar;Zhenyu Yang.
(2013)

305 Citations

Differential evolution for high-dimensional function optimization

Zhenyu Yang;Ke Tang;Xin Yao.
congress on evolutionary computation (2007)

218 Citations

Population-Based Algorithm Portfolios for Numerical Optimization

Fei Peng;Ke Tang;Guoliang Chen;Xin Yao.
IEEE Transactions on Evolutionary Computation (2010)

212 Citations

Large-scale global optimization using cooperative coevolution with variable interaction learning

Wenxiang Chen;Thomas Weise;Zhenyu Yang;Ke Tang.
parallel problem solving from nature (2010)

193 Citations

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Best Scientists Citing Ke Tang

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University of Ontario Institute of Technology

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Xiaodong Li

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Nanyang Technological University

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Xingyi Zhang

Anhui University

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Xidian University

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CINVESTAV

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Francisco Herrera

University of Granada

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