在跟踪动态最优值时，种群可能至关重要
Populations can be essential in tracking dynamic optima
摘要：
现实世界的优化问题通常是动态的。由于目标和约束条件的变化，必须更新或替换以前好的解决方案。人们经常声称，进化算法特别适合于动态优化，因为大量的人口可以包含不同的解决方案，可能是有用的，在未来。然而，严格的理论论证如何在动态优化人口可以是必不可少的稀疏和限制在特殊情况下。本文提供了理论解释如何人口可以是必不可少的进化动态优化在一般和自然的设置。我们描述了一个自然类的动态优化问题，一个足够大的人口是必要的，以保持可靠的移动最优跟踪。我们建立了人口规模和算法失去最优跟踪的概率之间的关系。
英文摘要：
Real-world optimisation problems are often dynamic. Previously good solutions must be updated or replaced due to changes in objectives and constraints. It is often claimed that evolutionary algorithms are particularly suitable for dynamic optimisation because a large population can contain different solutions that may be useful in the future. However, rigorous theoretical demonstrations for how populations in dynamic optimisation can be essential are sparse and restricted to special cases. This paper provides theoretical explanations of how populations can be essential in evolutionary dynamic optimisation in a general and natural setting. We describe a natural class of dynamic optimisation problems where a sufficiently large population is necessary to keep track of moving optima reliably. We establish a relationship between the population-size and the probability that the algorithm loses track of the optimum.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
