2 citations · 4 across the 6 of their papers we have counts for
6 papers
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…
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…
Maintaining Diversity Provably Helps in Evolutionary Multimodal Optimization
Shengjie Ren, Zhijia Qiu, Chao Bian +2
In the real world, there exist a class of optimization problems that multiple (local) optimal solutions in the solution space correspond to a single point in the objective space. I…
An Archive Can Bring Provable Speed-ups in Multi-Objective Evolutionary Algorithms
Chao Bian, Shengjie Ren, Miqing Li +1
In the area of multi-objective evolutionary algorithms (MOEAs), there is a trend of using an archive to store non-dominated solutions generated during the search. This is because 1…
A First Running Time Analysis of the Strength Pareto Evolutionary Algorithm 2 (SPEA2)
Shengjie Ren, Chao Bian, Miqing Li +1
Evolutionary algorithms (EAs) have emerged as a predominant approach for addressing multi-objective optimization problems. However, the theoretical foundation of multi-objective EA…
Towards Running Time Analysis of Interactive Multi-objective Evolutionary Algorithms
Tianhao Lu, Chao Bian, Chao Qian
Evolutionary algorithms (EAs) are widely used for multi-objective optimization due to their population-based nature. Traditional multi-objective EAs (MOEAs) generate a large set of…