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.
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.
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.
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.
Evolutionary optimization in uncertain environments-a survey
Yaochu Jin;J. Branke.
IEEE Transactions on Evolutionary Computation (2005)
A comprehensive survey of fitness approximation in evolutionary computation
soft computing (2005)
Surrogate-assisted evolutionary computation: Recent advances and future challenges
Swarm and evolutionary computation (2011)
A framework for evolutionary optimization with approximate fitness functions
Yaochu Jin;M. Olhofer;B. Sendhoff.
IEEE Transactions on Evolutionary Computation (2002)
RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm
Qingfu Zhang;Aimin Zhou;Yaochu Jin.
IEEE Transactions on Evolutionary Computation (2008)
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems (2000)
A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization
Ran Cheng;Yaochu Jin;Markus Olhofer;Bernhard Sendhoff.
IEEE Transactions on Evolutionary Computation (2016)
A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization
Xingyi Zhang;Ye Tian;Yaochu Jin.
IEEE Transactions on Evolutionary Computation (2015)
Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies
Yaochu Jin;B. Sendhoff.
systems man and cybernetics (2008)
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum]
Ye Tian;Ran Cheng;Xingyi Zhang;Yaochu Jin.
IEEE Computational Intelligence Magazine (2017)
Complex & Intelligent Systems
(Impact Factor: 6.7)
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: