演化机器学习
演化机器学习是人工智能的一个子领域，它结合了演化算法和机器学习的原理。它旨在通过模仿自然演化过程来创建智能系统。在演化机器学习中，候选解决方案群体通过选择、交叉和突变等过程，经过多代演化。这种迭代过程可使系统随着时间的推移不断适应并提高性能。通过利用演化的力量，演化机器学习可以解决复杂的优化和学习问题，使其适用于数据挖掘、机器人和优化等领域的各种应用。
- 课题组论文
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1 Yu Zhou Yingyu Peng Ruiqi Wang Dandan Yu
Feature Selection for High-Dimensional Data Based on a Multi-objective Particle Swarm Optimization with Self-adjusting Strategy Pool
Neural Computing for Advanced Applications( NCAA 2022 ) Link
2 Yu Zhou Hainan Guo Junnan Ma Ruiqi Wang
Feature library-assisted surrogate model for evolutionary wrapper-based feature selection and classification
Applied Soft Computing( ASOC ) Link Code
3 Yu Zhou Yan Qiu Sam Kwong
Region Purity-based Local Feature Selection: A Multi-Objective Perspective
IEEE Transactions on Evolutionary Computation ( TEVC ) Link Code
4 Yu Zhou Jiping Lin Hainan Guo
Feature subset selection via an improved discretization-based particle swarm optimization
Applied Soft Computing ( ASOC ) Link
5 Yu Zhou Junhao Kang Sam Kwong Xu Wang Qingfu Zhang
An evolutionary multi-objective optimization framework of discretization-based feature selection for classification
Swarm and Evolutionary Computation ( SWEVO ) Link
6 Yu Zhou Wenjun ZhangJunhao Kang Xiao Zhang Xu Wang
A problem-specific non-dominated sorting genetic algorithm for supervised feature selection
Information Sciences ( INS ) Link Code
7 Yu Zhou Junhao Kang Hainan Guo
Many-objective optimization of feature selection based on two-level particle cooperation
Information Sciences ( INS ) Link Code
8 Yu Zhou Mengyuan Wu Ke Li Sam Kwong Qingfu Zhang
Matching-Based Selection With Incomplete Lists for Decomposition Multiobjective Optimization
IEEE Transactions on Evolutionary Computation ( TEVC ) Link
9 Yu Zhou Xiao Zhang Qingfu Zhang Victor C. S. Lee Minming Li
Problem Specific MOEA/D for Barrier Coverage with Wireless Sensors
IEEE Transactions on Cybernetics( TCYB ) Link