Slightly beyond Turing’s computability for
studying genetic programming
Abstract. Inspired by genetic programming (GP), we study iterative algorithms for non-computable tasks and compare them to naive models. This framework justiﬁes many practical standard tricks from GP and also provides complexity lower-bounds which justify the computational cost of GP thanks to the use of Kolmogorov’s complexity in bounded time.