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pro vyhledávání: '"Kay Chen Tan"'
Publikováno v:
IEEE Transactions on Cybernetics. :1-14
Publikováno v:
IEEE Transactions on Cybernetics. :1-13
Publikováno v:
IEEE Transactions on Cybernetics. :1-14
Publikováno v:
IEEE Transactions on Cybernetics. 52:13654-13668
It is known that many-objective optimization problems (MaOPs) often face the difficulty of maintaining good diversity and convergence in the search process due to the high-dimensional objective space. To address this issue, this article proposes a no
Autor:
Kai Zhang, Xinggang Zhao, Guodong Chen, Liming Zhang, Jun Yao, Liang Feng, Jian Wang, Xiaoming Xue, Kay Chen Tan
Publikováno v:
IEEE Transactions on Cybernetics. 52:6217-6231
Evolutionary multitasking (EMT) is a newly emerging research topic in the community of evolutionary computation, which aims to improve the convergence characteristic across multiple distinct optimization tasks simultaneously by triggering knowledge t
Publikováno v:
IEEE Transactions on Cybernetics. 52:6745-6758
The searching ability of the population-based search algorithms strongly relies on the coordinate system on which they are implemented. However, the widely used coordinate systems in the existing multifactorial optimization (MFO) algorithms are still
Publikováno v:
IEEE Transactions on Cybernetics. 52:3841-3854
In multilabel learning, each training example is represented by a single instance, which is relevant to multiple class labels simultaneously. Generally, all relevant labels are considered to be available for labeled data. However, instances with a fu
Publikováno v:
IEEE Transactions on Cybernetics. 52:3495-3509
Performance of multi/many-objective evolutionary algorithms (MOEAs) based on decomposition is highly impacted by the Pareto front (PF) shapes of multi/many-objective optimization problems (MOPs), as their adopted weight vectors may not properly fit t
Publikováno v:
IEEE Transactions on Cybernetics. :1-14
Decomposition methods have been widely employed in evolutionary algorithms for tackling multiobjective optimization problems (MOPs) due to their good mathematical explanation and promising performance. However, most decomposition methods only use a s
Publikováno v:
IEEE Transactions on Cybernetics. :1-13
Surrogate-assisted evolutionary algorithms (SAEAs) have shown promising performance for solving expensive optimization problems (EOPs) whose true evaluations are computationally or physically expensive. However, most existing SAEAs only focus on the