Skip to content
Log in

Author

Miqing Li

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

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

Evolutionary algorithms (EAs) are popular for multi-objective optimization due to their population-based nature. While population updates in multi-objective EAs (MOEAs) are typically greedy and deterministic. However, recent studies have questioned this practice and shown that stochastic population update (SPU), which allows inferior solutions have a chance to be preserved, can help MOEAs jump out of local optima more easily. Nevertheless, SPU risks losing high-quality solutions, potentially requiring a large population. Intuitively, a possible solution to this issue is to introduce an archive that stores the best solutions ever found. This paper theoretically demonstrates that incorporating an archive to store best-found solutions enables smaller populations and enhances SPU-based MOEA performance. Analyzing SMS-EMOA and NSGA-II on the bi-objective OneJumpZeroJump problem, we prove archives reduce expected running time upper bounds (even exponentially). The comparison between SMS-EMOA and NSGA-II also suggests that the (μ + μ) update mode may be more suitable for SPU than the (μ + 1) update mode. We also validate our findings empirically. This paper for the Hot-off-the-Press track at GECCO 2026 sum marizes the work S. Ren, Z. Liang, M. Li, and C. Qian. A Theoretical Perspective on Why Stochastic Population Update Needs an Archive in Evolutionary Multi-objective Optimization. IJCAI, 2025, 8921: 8929. [17]

Shengjie Ren, Zimin Liang, Miqing Li et al. · 0 citations
Book Open access Jul 2026

Hot off the Press: Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms

Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-objective optimization problems, due to their nature of population-based search. Population update, a key component in multi-objective EAs (MOEAs), is usually performed in a greedy, deterministic manner. In this paper, we analytically present that stochastic population update can be beneficial for the search of MOEAs. Specifically, we prove that the expected running time of two well-established MOEAs, SMS-EMOA and NSGA-II, for solving two bi-objective problems, OneJumpZeroJump and bi-objective RealRoyalRoad, can be exponentially decreased if replacing its deterministic population update mechanism by a stochastic one. Empirical studies also verify the effectiveness of the proposed population update method. This work is an attempt to show the benefit of introducing randomness into the population update of MOEAs. Its positive results, which might hold more generally, should encourage the exploration of developing new MOEAs in the area. This paper for the Hot-off-the-Press track at GECCO 2025 summarizes the work C. Bian, Y. Zhou, M. Li, and C. Qian. Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms. Artificial Intelligence, 2025, 341: 104308. [5]

Chao Bian, Yawen Zhou, Miqing Li et al. · 0 citations