We show that if evolution is algorithmic in any form and can thus be considered a program in software space, the emergence of a natural algorithmic probability distribution has the potential to become an accelerating mechanism. We simulate the application of algorithmic mutations to binary matrices based on numerical approximations to algorithmic probability, comparing the evolutionary speed to the alternative hypothesis of uniformly distributed mutations for a series of matrices of varying complexity. When the algorithmic mutation produces unfit organisms—because mutations may lead to, for example, syntactically useless evolutionary programs—massive extinctions may occur. We show that modularity provides an evolutionary advantage also evolving a genetic memory. We demonstrate that such regular structures are preserved and carried on when they first occur and can also lead to an accelerated production of diversity and extinction, possibly explaining natural phenomena such as periods of accelerated growth of the number of species (e.g. the Cambrian explosion) and the occurrence of massive extinctions (e.g. the End Triassic) whose causes are a matter of considerable debate. The approach introduced here appears to be a better approximation to actual biological evolution than models based upon the application of mutation from uniform probability distributions, and because evolution by algorithmic probability converges faster to regular structures (both artificial and natural, as tested on a small biological network), it also approaches a formal version of open-ended evolution based on previous results. The results validate the motivations and results of Chaitin’s Metabiology programme. We also show that the procedure has the potential to significantly accelerate solving optimization problems in the context of artificial evolutionary algorithms.

Algorithmically probable mutations reproduce aspects of evolution such as convergence rate, genetic memory, modularity, diversity explosions, and mass extinction

Santiago Hernández-Orozco, Hector Zenil, Narsis A. Kiani

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Source: arxiv.org

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