When searching for input configurations that optimise the output of a system, it can be useful to build a statistical model of the system being optimised. This is done in approaches such as surrogate model-based optimisation, estimation of distribution algorithms, and linkage learning algorithms. This article presents a method for modelling pseudo-Boolean fitness functions using Walsh bases and an algorithm designed to discover the non-zero coefficients while attempting to minimise the number of fitness function evaluations required. The resulting models reveal linkage structure that can be used to guide a search of the model efficiently. It presents experimental results solving benchmark problems in fewer fitness function evaluations than those reported in the literature for other search methods such as EDAs and linkage learners.

中文翻译：

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从小样本中学习和搜索伪布尔代理函数

在搜索优化系统输出的输入配置时，构建被优化系统的统计模型会很有用。这是在基于代理模型的优化、分布估计算法和链接学习算法等方法中完成的。本文介绍了一种使用 Walsh 基对伪布尔适应度函数进行建模的方法，以及一种旨在发现非零系数的算法，同时尝试最小化所需的适应度函数评估次数。生成的模型揭示了可用于有效指导模型搜索的链接结构。与文献中报道的其他搜索方法（如 EDA 和链接学习器）相比，它提供了在更少的适应度函数评估中解决基准问题的实验结果。