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
Computer Science D-index 80 Citations 25,094 379 World Ranking 425 National Ranking 25

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

Yaochu Jin mainly focuses on Mathematical optimization, Evolutionary algorithm, Evolutionary computation, Multi-objective optimization and Artificial intelligence. His work on Optimization problem, Metaheuristic and Imperialist competitive algorithm as part of general Mathematical optimization research is often related to Quality, thus linking different fields of science. His studies deal with areas such as Computational intelligence, Genetic algorithm, Algorithm, Approximation algorithm and Fitness approximation as well as Evolutionary algorithm.

In his research on the topic of Evolutionary computation, Point and Computational complexity theory is strongly related with Convergence. His Multi-objective optimization research includes themes of Pareto principle, Estimation of distribution algorithm, Test functions for optimization and Benchmark. His Artificial intelligence study combines topics in areas such as Machine learning and Reduction.

His most cited work include:

  • Evolutionary optimization in uncertain environments-a survey (1277 citations)
  • A comprehensive survey of fitness approximation in evolutionary computation (927 citations)
  • Surrogate-assisted evolutionary computation: Recent advances and future challenges (683 citations)

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

Yaochu Jin focuses on Evolutionary algorithm, Mathematical optimization, Artificial intelligence, Multi-objective optimization and Machine learning. His studies in Evolutionary algorithm integrate themes in fields like Genetic algorithm, Fitness function, Selection, Benchmark and Evolutionary computation. His research brings together the fields of Convergence and Mathematical optimization.

His work deals with themes such as Gene regulatory network and Pattern recognition, which intersect with Artificial intelligence. His work carried out in the field of Multi-objective optimization brings together such families of science as Estimation of distribution algorithm, Test functions for optimization and Robustness. His Optimization problem research includes elements of Linear programming and Computational intelligence.

He most often published in these fields:

  • Evolutionary algorithm (47.34%)
  • Mathematical optimization (45.87%)
  • Artificial intelligence (44.04%)

What were the highlights of his more recent work (between 2018-2021)?

  • Evolutionary algorithm (47.34%)
  • Mathematical optimization (45.87%)
  • Artificial intelligence (44.04%)

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

Yaochu Jin mainly investigates Evolutionary algorithm, Mathematical optimization, Artificial intelligence, Benchmark and Optimization problem. His Evolutionary algorithm research integrates issues from Computational intelligence, Artificial neural network, Multi-objective optimization, Evolutionary computation and Robustness. His Mathematical optimization research focuses on Convergence and how it connects with Sorting.

His research investigates the link between Artificial intelligence and topics such as Machine learning that cross with problems in Cancer and Network architecture. The Benchmark study which covers Fitness function that intersects with Anomaly detection and Data stream. The Optimization problem study combines topics in areas such as Human multitasking, Function, Taxonomy, Linear programming and Function.

Between 2018 and 2021, his most popular works were:

  • Data-Driven Evolutionary Optimization: An Overview and Case Studies (92 citations)
  • Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation (90 citations)
  • A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization (86 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary areas of study are Mathematical optimization, Evolutionary algorithm, Benchmark, Artificial intelligence and Multi-objective optimization. His Optimization problem, Evolutionary computation and Multiobjective optimization problem study, which is part of a larger body of work in Mathematical optimization, is frequently linked to Gaussian process, bridging the gap between disciplines. His Evolutionary computation research is multidisciplinary, incorporating perspectives in Performance indicator and Complex network.

Yaochu Jin has researched Evolutionary algorithm in several fields, including Artificial neural network, Particle swarm optimization, Adversarial system and Test suite. His Artificial intelligence study frequently draws connections to adjacent fields such as Machine learning. Yaochu Jin works mostly in the field of Multi-objective optimization, limiting it down to concerns involving Pareto principle and, occasionally, Distributed computing and Multiobjective optimization algorithm.

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

Evolutionary optimization in uncertain environments-a survey

Yaochu Jin;J. Branke.
IEEE Transactions on Evolutionary Computation (2005)

1681 Citations

A comprehensive survey of fitness approximation in evolutionary computation

Y. Jin.
soft computing (2005)

1248 Citations

Surrogate-assisted evolutionary computation: Recent advances and future challenges

Yaochu Jin.
Swarm and evolutionary computation (2011)

709 Citations

A framework for evolutionary optimization with approximate fitness functions

Yaochu Jin;M. Olhofer;B. Sendhoff.
IEEE Transactions on Evolutionary Computation (2002)

644 Citations

RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm

Qingfu Zhang;Aimin Zhou;Yaochu Jin.
IEEE Transactions on Evolutionary Computation (2008)

617 Citations

Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement

Yaochu Jin.
IEEE Transactions on Fuzzy Systems (2000)

573 Citations

A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization

Ran Cheng;Yaochu Jin;Markus Olhofer;Bernhard Sendhoff.
IEEE Transactions on Evolutionary Computation (2016)

478 Citations

A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization

Xingyi Zhang;Ye Tian;Yaochu Jin.
IEEE Transactions on Evolutionary Computation (2015)

383 Citations

Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies

Yaochu Jin;B. Sendhoff.
systems man and cybernetics (2008)

361 Citations

PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum]

Ye Tian;Ran Cheng;Xingyi Zhang;Yaochu Jin.
IEEE Computational Intelligence Magazine (2017)

351 Citations

Editorial Boards

Complex & Intelligent Systems
(Impact Factor: 6.7)

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