27 citations · 62 across the 5 of their papers we have counts for
5 papers
An Online Prediction Approach Based on Incremental Support Vector Machine for Dynamic Multiobjective Optimization
Dejun Xu, Min Jiang, Weizhen Hu +3
Real-world multiobjective optimization problems usually involve conflicting objectives that change over time, which requires the optimization algorithms to quickly track the Pareto…
Evolutionary Dynamic Multi-objective Optimization Via Regression Transfer Learning
Zhenzhong Wang, Min Jiang, Xing Gao +3
Dynamic multi-objective optimization problems (DMOPs) remain a challenge to be settled, because of conflicting objective functions change over time. In recent years, transfer learn…
Online Bagging for Anytime Transfer Learning
Guokun Chi, Min Jiang, Xing Gao +3
Transfer learning techniques have been widely used in the reality that it is difficult to obtain sufficient labeled data in the target domain, but a large amount of auxiliary data…
Solving Dynamic Multi-objective Optimization Problems Using Incremental Support Vector Machine
Weizhen Hu, Min Jiang, Xing Gao +2
The main feature of the Dynamic Multi-objective Optimization Problems (DMOPs) is that optimization objective functions will change with times or environments. One of the promising…
Solving dynamic multi-objective optimization problems via support vector machine
Min Jiang, Weizhen Hu, Liming Qiu +2
Dynamic Multi-objective Optimization Problems (DMOPs) refer to optimization problems that objective functions will change with time. Solving DMOPs implies that the Pareto Optimal S…