AbstractWe consider a scalable problem that has strong ties with real-world problems, can be compactly'/> The importance of implementation details and parameter settings in black-box optimization: a case study on Gaussian estimation-of-distribution algorithms and circles-in-a-square packing problems
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首页> 外文期刊>Soft computing: A fusion of foundations, methodologies and applications >The importance of implementation details and parameter settings in black-box optimization: a case study on Gaussian estimation-of-distribution algorithms and circles-in-a-square packing problems
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The importance of implementation details and parameter settings in black-box optimization: a case study on Gaussian estimation-of-distribution algorithms and circles-in-a-square packing problems

机译：黑盒优化中实施细节和参数设置的重要性：以高斯分布分布算法和圈子 - 一方面包装问题为例

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AbstractWe consider a scalable problem that has strong ties with real-world problems, can be compactly formulated and efficiently evaluated, yet is not trivial to solve and has interesting characteristics that differ from most commonly used benchmark problems: packingncircles in a square (CiaS). Recently, a first study that used basic Gaussian EDAs indicated that typically suggested algorithmic parameter settings do not necessarily transfer well to the CiaS problem. In this article, we consider also AMaLGaM, an enhanced Gaussian EDA, as well as arguably the most powerful real-valued black-box optimization algorithm to date, CMA-ES, which can also be seen as a further enhanced Gaussian EDA. We study whether the well-known performance on typical benchmark problems extends to the CiaS problem. We find that although the enhancements over a basic Gaussian EDA result in superior performance, the further efficiency enhancements in CMA-ES are not highly impactful. Instead, the most impactful features are how constraint handling is performed, how large the population size is, whether a full covariance matrix is used and whether restart techniques are used. We further show that a previously published version of AMaLGaM that does not require the user to set the the population size parameter is capable of solving the problem and we derive the scalability of the required number of function evaluations to solve the problem up to 99.99?% of the known optimal value for up to 30 circles.]]>
机译： n 圆圈（ cias）。最近，使用基本高斯EDA的第一研究表明，通常建议的算法参数设置不一定对CIAS问题转移得很好。在本文中，我们考虑了Amalgam，一个增强的高斯EDA，以及迄今为止的最强大的真实的黑匣子优化算法，CMA-ES也可以被视为进一步增强的高斯EDA。我们研究典型的基准问题上的知名性能是否扩展到CIAS问题。我们发现，尽管对基本高斯EDA的增强导致了卓越的性能，但CMA-ES的进一步效率增强并不产生强烈影响。相反，最有影响力的特征是如何执行约束处理，人口大小是多大的，无论是否使用完整的协方差矩阵以及是否使用重新启动技术。我们进一步表明，先前发布的Amalgam版本不要求用户设置人口大小参数能够解决问题，我们导出了所需数量的函数评估的可伸缩性，以解决99.99的问题最多30个圆圈的已知最佳值。 ]]>

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