进化算法中，停滞反而助收敛，收敛不等于最优。
Stagnation in Evolutionary Algorithms: Convergence $\neq$ Optimality
- 个体停滞反促种群整体收敛
- 收敛后仍可能未达局部最优
- 适合研究算法本质的学者
在进化计算领域，普遍认为停滞会阻碍进化算法的收敛，且收敛即意味着最优。然而这一观点具有误导性。本研究首次指出，个体停滞实际上可能促进整个种群的收敛，且收敛并不必然代表最优性，甚至无法保证局部最优。仅靠收敛无法确保进化算法的有效性。文中通过多个反例阐明该论点。
原文摘要 · Abstract (English)
In the evolutionary computation community, it is widely believed that stagnation impedes convergence in evolutionary algorithms, and that convergence inherently indicates optimality. However, this perspective is misleading. In this study, it is the first to highlight that the stagnation of an individual can actually facilitate the convergence of the entire population, and convergence does not necessarily imply optimality, not even local optimality. Convergence alone is insufficient to ensure the effectiveness of evolutionary algorithms. Several counterexamples are provided to illustrate this argument.
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