48 citations · 134 across the 20 of their papers we have counts for
7 papers · 1 filter
Uncertainty-Aware Offline Data-Driven Multi-Objective Optimization
Huanbo Lyu, Miqing Li, Shiqiao Zhou +7
In offline data-driven multi-objective optimization (MOO), optimization is performed using surrogate models trained only on an offline dataset. These surrogate models contain inher…
Not Just for Archiving: Provable Benefits of Reusing the Archive in Evolutionary Multi-objective Optimization
Shengjie Ren, Zimin Liang, Miqing Li +1
Evolutionary Algorithms (EAs) have become the most popular tool for solving widely-existed multi-objective optimization problems. In Multi-Objective EAs (MOEAs), there is increasin…
When to Truncate the Archive? On the Effect of the Truncation Frequency in Multi-Objective Optimisation
Zhiji Cui, Zimin Liang, Lie Meng Pang +2
Using an archive to store nondominated solutions found during the search of a multi-objective evolutionary algorithm (MOEA) is a useful practice. However, as nondominated solutions…
On the Problem Characteristics of Multi-objective Pseudo-Boolean Functions in Runtime Analysis
Zimin Liang, Miqing Li
Recently, there has been growing interest within the theoretical community in analytically studying multi-objective evolutionary algorithms. This runtime analysis-focused research…
A Weight Adaptation Trigger Mechanism in Decomposition-based Evolutionary Multi-Objective Optimisation
Xiaofeng Han, Xiaochen Chu, Tao Chao +2
Decomposition-based multi-objective evolutionary algorithms (MOEAs) are widely used for solving multi-objective optimisation problems. However, their effectiveness depends on the c…
A Theoretical Perspective on Why Stochastic Population Update Needs an Archive in Evolutionary Multi-objective Optimization
Shengjie Ren, Zimin Liang, Miqing Li +1
Evolutionary algorithms (EAs) have been widely applied to multi-objective optimization due to their population-based nature. Population update, a key component in multi-objective E…
Distilled Lifelong Self-Adaptation for Configurable Systems
Yulong Ye, Tao Chen, Miqing Li
Modern configurable systems provide tremendous opportunities for engineering future intelligent software systems. A key difficulty thereof is how to effectively self-adapt the conf…