Symbolic regression (SR), as a special machine learning method, can produce mathematical models with explicit expressions. It has received increasing attention in recent years. However, finding a concise, accurate expression is still challenging because of its huge search space. In this work, a divide and conquer (D&C) scheme is proposed. It tries to divide the search space into a number of orthogonal sub-spaces based on the separability feature inferred from the sample data (dividing process). For each sub-space, a sub-function is learned (conquering process). The target model function is then reconstructed with the sub-functions according to their separability patterns. To this end, a separability pattern detecting technique, bi-correlation test (Bi-CT), is also proposed. Note that the sub-functions could be determined by any of the existing SR methods, which makes D&C easy to use. The D&C powered SR has been tested on many symbolic regression problems, and the study shows that D&C can help SR to get the target function more quickly and reliably.

符号回归（SR）作为一种特殊的机器学习方法，可以产生具有显式表达式的数学模型。近年来受到越来越多的关注。然而，由于其巨大的搜索空间，找到一个简洁、准确的表达仍然具有挑战性。在这项工作中，提出了分而治之（D＆C）方案。它试图根据从样本数据推断出的可分离性特征（划分过程）将搜索空间划分为多个正交子空间。对于每个子空间，学习一个子函数（征服过程）。然后根据子函数的可分离性模式重建目标模型函数。为此，还提出了一种可分离模式检测技术，即双相关测试（Bi-CT）。请注意，子功能可以由任何现有的 SR 方法确定，这使得 D&C 易于使用。D&C 驱动的 SR 已经在许多符号回归问题上进行了测试，研究表明 D&C 可以帮助 SR 更快、更可靠地获得目标函数。