“…Normally, GP implements an algorithm that uses random crossover, mutation, a fitness function, and multiple generations of evolution to resolve a user‐defined task, which makes it applicable to automatically discovering a functional relationship between features in data (i.e., symbolic regression, instead of traditional numerical regression). Generally, GP gives each solution in a tree structure (Figure ), with an operator function (e.g., the four rules of arithmetic, trigonometric functions, exponent, and logarithm) in every tree node and an operand (e.g., variable and number) in every terminal node, necessitating the evaluation of mathematical and logical expressions (Liu & Shi, ). Crossover and mutation are the two major processes for producing new individuals.…”