Author

Shengjie Ren

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Conference Open access Sep 2026

One for Exploration and Another for Exploitation: A Dual-Population MOEA Framework with Provable Benefits

Evolutionary Algorithms (EAs) are currently the most popular tool for solving multi-objective optimization problems. Balancing exploration and exploitation is fundamental to the performance of Multi-Objective EAs (MOEAs). Achieving this requires maintaining a set of high-quality solutions for effective exploitation whi...

Cheng-Lin Jiang, Sheng-Jie Ren, Zi-Min Liang et al. · Proceedings of the Thirty-Fi... · 3 citations

Conference Open access Sep 2026

Theoretical Analysis of Multi-Objective Evolutionary Algorithms on Integer Spaces with Local Optima

Multi-objective evolutionary algorithms (MOEAs) are popular tools for multi-objective optimization (MOO), and have been successfully applied to many real-world MOO problems. However, the theoretical study has lagged behind their practical success and remains largely confined to synthetic pseudo-Boolean functions. To cl...

Yue-Tong Sun, Zeqiong Lv, Sheng-Jie Ren et al. · Proceedings of the Thirty-Fi... · 0 citations

Book Open access Jul 2026

Hot off the Press: A Theoretical Perspective on Why Stochastic Population Update Needs an Archive in Evolutionary Multi-objective Optimization

This paper theoretically demonstrates that incorporating an archive to store best-found solutions enables smaller populations and enhances SPU-based MOEA performance and proves archives reduce expected running time upper bounds (even exponentially).

Sheng-Jie Ren, Zi-Min Liang, Mi-Qing Li et al. · GECCO Companion · 0 citations