PAC learning and genetic programming

Genetic programming (GP) is a very successful type of learning algorithm that is hard to understand from a theoretical point of view. With this paper we contribute to the computational complexity analysis of genetic programming that has been started recently. We analyze GP in the well-known PAC learning framework and point out how it can observe quality changes in the the evolution of functions by random sampling. This leads to computational complexity bounds for a linear GP algorithm for perfectly learning any member of a simple class of linear pseudo-Boolean functions. Furthermore, we show that the same algorithm on the functions from the same class finds good approximations of the target function in less time.Timo Kötzing, Frank Neumann and Reto Spöhe