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
Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In MOBO, the goal is to adequately approximate the Pareto front; that is, to obtain a high-quality solution set with 1) good convergence (closeness to the Pareto front) and...
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
This work analytically present that stochastic population update can be beneficial for the search of MOEAs, and proves that the expected running time of two well-established MOEAs, SMS-EMOA and NSGA-II, for solving two bi-objective problems can be exponentially decreased if replacing its deterministic population update...
Chao Bian, Yawen Zhou, Miqing Li et al.· GECCO Companion· 0 citations
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