I. Introduction
Genetic programming (GP) [1] is a popular evolutionary computation (EC) technique. GP draws inspiration from the Darwinian theory of evolution to optimize programs for user-defined tasks. This process involves multiple generations of evolution, during which programs experience selection, crossover, mutation, and fitness evaluation to improve their performance. Owing to its flexible representation, good global search ability, and high interpretability of the evolved solutions, GP has been widely applied to many learning tasks, such as classification [2], regression [3], [4], and feature learning [5], [6].