In genetic programming (GP), computer programs are often coevolved with training data subsets that are known as fitness predictors. In order to maximize performance of GP, it is important to find the most suitable parameters of coevolution, particularly the fitness predictor size. This is a very time-consuming process as the predictor size depends on a given application, and many experiments have to be performed to find its suitable size. A new method is proposed which enables us to automatically adapt the predictor and its size for a given problem and thus to reduce not only the time of evolution, but also the time needed to tune the evolutionary algorithm. The method was implemented in the context of Cartesian genetic programming and evaluated using five symbolic regression problems and three image filter design problems. In comparison with three different CGP implementations, the time required by CGP search was reduced while the quality of results remained unaffected.

中文翻译：

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协同进化笛卡尔遗传规划中的自适应适应度预测器

在遗传编程 (GP) 中，计算机程序通常与被称为适应度预测器的训练数据子集共同进化。为了最大限度地提高 GP 的性能，重要的是找到最合适的协同进化参数，尤其是适应度预测器的大小。这是一个非常耗时的过程，因为预测器的大小取决于给定的应用程序，并且必须进行许多实验才能找到合适的大小。提出了一种新方法，使我们能够针对给定问题自动调整预测器及其大小，从而不仅减少进化时间，而且减少调整进化算法所需的时间。该方法在笛卡尔遗传编程的背景下实施，并使用五个符号回归问题和三个图像滤波器设计问题进行评估。