Clearing is a niching method inspired by the principle of assigning the available resources among a niche to a single individual. The clearing procedure supplies these resources only to the best individual of each niche: the winner. So far, its analysis has been focused on experimental approaches that have shown that clearing is a powerful diversity-preserving mechanism. Using rigorous runtime analysis to explain how and why it is a powerful method, we prove that a mutation-based evolutionary algorithm with a large enough population size, and a phenotypic distance function always succeeds in optimising all functions of unitation for small niches in polynomial time, while a genotypic distance function requires exponential time. Finally, we prove that with phenotypic and genotypic distances, clearing is able to find both optima for TwoMax and several general classes of bimodal functions in polynomial expected time. We use empirical analysis to highlight some of the characteristics that makes it a useful mechanism and to support the theoretical results.

中文翻译：

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关于清除多样性保护机制的运行时分析

清理是一种利基方法，其灵感来自将利基之间的可用资源分配给单个个体的原则。清算程序仅将这些资源提供给每个利基市场的最佳个人：获胜者。到目前为止，它的分析一直集中在实验方法上，这些方法表明清除是一种强大的多样性保护机制。使用严格的运行时分析来解释它如何以及为什么它是一种强大的方法，我们证明了具有足够大的种群规模和表型距离函数的基于突变的进化算法总是能够在多项式时间内成功优化所有小生态位的统一函数，而基因型距离函数需要指数时间。最后，我们证明，通过表型和基因型距离，clearing 能够在多项式预期时间内找到 TwoMax 和几个一般双峰函数类的最优值。我们使用实证分析来突出使其成为有用机制的一些特征并支持理论结果。