We can measure a program’s fitness (or lack thereof) by the distance between its output and the correct one on the training data. For example, if the program says an Earth year is three hundred days, that would subtract sixty-five points from its fitness. Starting with a population of random program trees, genetic programming uses crossover, mutation, and survival to gradually evolve better programs until it’s satisfied.
