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新研究分析了进化算法在动态环境中的性能

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新研究论文探讨了进化算法（EAs）在动态环境中的性能。第一篇论文分析了动态线性环境中的（1+1）-EA，证明了突变率存在一个严格的阈值，该阈值决定了优化时间是多项式还是指数级。第二篇论文聚焦于二元值（BinVal）函数的（μ+1）EA，建立了一个显著改进的运行时间界限，表明其比在OneMax函数上仅慢对数级。 AI

排序理由 该集群包含两篇在arXiv上发表的学术论文，详细介绍了进化算法的理论分析。

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

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新研究分析了进化算法在动态环境中的性能

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该集群包含两篇在arXiv上发表的学术论文，详细介绍了进化算法的理论分析。
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报道来源 [2]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Raghu Raman Ravi ·

    动态环境中的 $(1 + 1)$-EA

    We study the $(1 + 1)$-EA in dynamic linear environments, where in every generation selection is performed with respect to a freshly sampled linear function with positive weights. We consider the Dynamic Binary Value problem, where each generation uses a uniformly random permutat…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Raghu Raman Ravi ·

    Improved Runtime Bound for the $(μ+ 1)$ EA on BinVal

    We study the $(μ+1)$ EA on the Binary Value function BinVal. We show that it needs at most $O(μ\log μ\cdot n \log n)$ function evaluations to find the optimum when $μ= o(n/\log n)$. This substantially improves upon the recent upper bound of $O(μ^5 n \log(n/μ^4))$ by Krejca, Neuma…

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