Genetic Programming

Genetic programming (GP) is an evolutionary computation technique that automatically generates computer programs to solve problems by evolving populations of programs through mutation and recombination. Current research emphasizes improving GP's efficiency and scalability, particularly for complex tasks like neural architecture search, symbolic regression, and the synthesis of control systems, often incorporating techniques like lexicase selection, surrogate models, and deep learning operators to enhance performance. The ability of GP to produce interpretable models is driving its application in explainable AI and scientific discovery, offering a powerful tool for uncovering underlying patterns in data and generating human-understandable models across diverse fields.