Algorithmically probable mutations reproduce aspects of evolution such as convergence rate, genetic memory, modularity, diversity explosions, and mass extinction
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Natural selection explains how life has evolved over millions of years from more primitive forms. The speed at which this happens, however, has sometimes defied explanations based on random (uniformly distributed) mutations. Here we investigate the application of algorithmic mutations (no recombination) to binary matrices drawn from numerical approximations to algorithmic probability in order to compare evolutionary convergence rates against the null hypothesis (uniformly distributed mutations). Results both on synthetic and a small biological examples lead to an accelerated rate of convergence when using the algorithmic probability. We also show that algorithmically evolved modularity provides an advantage that produces a genetic memory. We demonstrate that regular structures are preserved and carried on when they first occur and can lead to an accelerated production of diversity and extinction, possibly explaining naturally occurring phenomena such as diversity explosions (e.g. the Cambrian) and massive extinctions (e.g. the End Triassic) whose causes have eluded researchers and are a cause for debate. The approach introduced here appears to be a better approximation to biological evolution than models based exclusively upon random uniform mutations, and it also approaches better a formal version of open-ended evolution based on previous results. The results validate the motivations and results of Chaitin's Metabiology programme and previous suggestions that computation may be an equally important driver of evolution together, and even before, the action and result of natural selection. We also show that inducing the method on problems of optimization, such as genetic algorithms, has the potential to significantly accelerate convergence of artificial evolutionary algorithms.
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