Abstract
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 formal explanations when based on random (uniformly distributed) mutations. Here, we investigate the application of a simplicity bias based on a natural but algorithmic distribution of mutations (no recombination) in various examples, particularly binary matrices, in order to compare evolutionary convergence rates. Results both on synthetic and on small biological examples indicate an accelerated rate when mutations are not statistically uniform but algorithmically uniform. We show that algorithmic distributions can evolve modularity and genetic memory by preservation of structures when they first occur sometimes leading to an accelerated production of diversity but also to population extinctions, possibly explaining naturally occurring phenomena such as diversity explosions (e.g. the Cambrian) and massive extinctions (e.g. the End Triassic) whose causes are currently a cause for debate. The natural approach introduced here appears to be a better approximation to biological evolution than models based exclusively upon random uniform mutations, and it also approaches a formal version of open-ended evolution based on previous formal results. These results validate some suggestions in the direction that computation may be an equally important driver of evolution. We also show that inducing the method on problems of optimization, such as genetic algorithms, has the potential to accelerate convergence of artificial evolutionary algorithms.
Highlights
Central to modern synthesis and general evolutionary theory is the understanding that evolution is gradual and is explained by small genetic changes in populations over time [1]
Non-coding DNA regions are subject to different mutation rates throughout the genome, because they are subject to less selective pressure than coding regions
What we will show here is that all other things being equal and without making considerations of other genetic operations beyond the scope of this paper, our results indicate that the operation of random mutation based on algorithmic probability and universal distribution (UD) makes ‘organisms’ converge faster and has interesting phenomenological implications such as modularity
Summary
Central to modern synthesis and general evolutionary theory is the understanding that evolution is gradual and is explained by small genetic changes in populations over time [1]. Genetic variation in populations can arise by chance through mutation, with these small changes leading to major evolutionary changes over time. Non-coding DNA regions are subject to different mutation rates throughout the genome, because they are subject to less selective pressure than coding regions. This is the same for the so-called microsatellites, repetitive DNA segments which are mostly non-coding, where the mutation rate increases as a function of number of repetitions. Beyond physical properties in which the probability of a given nucleotide mutating depends on its weaker or stronger chemo- and thermodynamic bonds, other departures from non-uniformity are less well understood, and seem to be the result of a process rather than being related to or driven by direct physical or chemical interactions
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