6 citations · 7 across the 5 of their papers we have counts for
5 papers
Pareto Front Shape-Agnostic Pareto Set Learning in Multi-Objective Optimization
Rongguang Ye, Longcan Chen, Wei-Bin Kou +2
Pareto set learning (PSL) is an emerging approach for acquiring the complete Pareto set of a multi-objective optimization problem. Existing methods primarily rely on the mapping of…
Learning Pareto Set for Multi-Objective Continuous Robot Control
Tianye Shu, Ke Shang, Cheng Gong +2
For a control problem with multiple conflicting objectives, there exists a set of Pareto-optimal policies called the Pareto set instead of a single optimal policy. When a multi-obj…
Evolutionary Preference Sampling for Pareto Set Learning
Rongguang Ye, Longcan Chen, Jinyuan Zhang +1
Recently, Pareto Set Learning (PSL) has been proposed for learning the entire Pareto set using a neural network. PSL employs preference vectors to scalarize multiple objectives, fa…
Data-Driven Preference Sampling for Pareto Front Learning
Rongguang Ye, Lei Chen, Weiduo Liao +2
Pareto front learning is a technique that introduces preference vectors in a neural network to approximate the Pareto front. Previous Pareto front learning methods have demonstrate…
Improving Critical Node Detection Using Neural Network-based Initialization in a Genetic Algorithm
Chanjuan Liu, Shike Ge, Zhihan Chen +4
The Critical Node Problem (CNP) is concerned with identifying the critical nodes in a complex network. These nodes play a significant role in maintaining the connectivity of the ne…