The process of developing new test statistics is laborious, requiring the manual development and evaluation of mathematical functions that satisfy several theoretical properties. Automating this process, hitherto not done, would greatly accelerate the discovery of much-needed, new test statistics. This automation is a challenging problem because it requires the discovery method to know something about the desirable properties of a good test statistic in addition to having an engine that can develop and explore candidate mathematical solutions with an intuitive representation. In this paper we describe a genetic programming-based system for the automated discovery of new test statistics. Specifically, our system was able to discover test statistics as powerful as the t test for comparing sample means from two distributions with equal variances.

中文翻译：

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使用遗传编程自动发现测试统计

开发新的测试统计的过程很费力，需要手动开发和评估满足多个理论属性的数学函数。自动化这个迄今为止尚未完成的过程，将大大加速发现急需的新测试统计数据。这种自动化是一个具有挑战性的问题，因为它需要发现方法除了拥有可以开发和探索具有直观表示的候选数学解决方案的引擎之外，还需要了解有关良好测试统计量的理想属性的一些信息。在本文中，我们描述了一个基于遗传编程的系统，用于自动发现新的测试统计数据。具体来说，我们的系统能够发现与 t 检验一样强大的检验统计量，用于比较来自具有相等方差的两个分布的样本均值。