If you like this see also [1], and Joy as well as Iota and Jot[2] (programming languages). And maybe "Algorithmically probable mutations reproduce aspects of evolution such as convergence rate, genetic memory, and modularity": https://arxiv.org/abs/1709.00268v8
> In the context of his Metabiology programme, Gregory Chaitin, a founder of the theory of algorithmic information, introduced a theoretical computational model that evolves ‘organisms’ relative to their environment considerably faster than classical random mutation. While theoretically sound, the ideas had not been tested and further advancements were needed for their actual implementation. Here we follow an experimental approach heavily based on the theory that Chaitin himself helped found. We apply his ideas on evolution operating in software space on synthetic and biological examples and even if further investigation is needed this work represents the first step towards testing and advancing a sound algorithmic framework for biological evolution.
> In the context of his Metabiology programme, Gregory Chaitin, a founder of the theory of algorithmic information, introduced a theoretical computational model that evolves ‘organisms’ relative to their environment considerably faster than classical random mutation. While theoretically sound, the ideas had not been tested and further advancements were needed for their actual implementation. Here we follow an experimental approach heavily based on the theory that Chaitin himself helped found. We apply his ideas on evolution operating in software space on synthetic and biological examples and even if further investigation is needed this work represents the first step towards testing and advancing a sound algorithmic framework for biological evolution.
[1] https://wiki.haskell.org/Chaitin%27s_construction
[2] https://www.nyu.edu/projects/barker/Iota/