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
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
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
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