Stagnation in Evolutionary Algorithms: Convergence $\neq$ Optimality
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917249896415232 |
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| author | Zhou, Xiaojun |
| author_facet | Zhou, Xiaojun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01036 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stagnation in Evolutionary Algorithms: Convergence $\neq$ Optimality Zhou, Xiaojun Machine Learning Artificial Intelligence 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. |
| title | Stagnation in Evolutionary Algorithms: Convergence $\neq$ Optimality |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2505.01036 |