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Motivated by algorithmic information theory, the problem of program discoverycan help find candidates of underlying generative mechanisms of natural andartificial phenomena. The uncomputability of such inverse problem, however,significantly restricts a wider application of exhaustive methods. Here wepresent a proof of concept of an approach based on IMP, a high-level imperativeprogramming language. Its main advantage is that conceptually complexcomputational routines are more succinctly expressed, unlike lower-level modelssuch as Turing machines or cellular automata. We investigate if a moreexpressive higher-level programming language can be more efficient atgenerating approximations to algorithmic complexity of recursive functions,often of particular mathematical interest.

Computable Model Discovery and High-Level-Programming Approximations to Algorithmic Complexity