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

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.

Slime mould: The fundamental mechanisms of biological cognition

“…In [183] and [184] it is shown how IIT is deeply and, in a formal setting, fundamentally connected to the concept of algorithmic complexity and data compression and in [185] how algorithmic probability-inversely related to algorithmic complexity by a formal theorem-may explain aspects of biological evolution. More recently, it has been shown that such foundations have the ability to explain a wide range of evolutionary phenomenology that would remain unexplained when making traditional assumptions related to the role of random mutation in natural selection and how organisms rather harness the highly structured nature of ecosystems [186].…”

Coding-theorem like behaviour and emergence of the universal distribution from resource-bounded algorithmic probability

“…molecular Brownian motion, the Universal Distribution may offer insights that may help us quantify the most likely regions if these laws in any sense constitute forms of computation below or at the Turing level that we explore here. This will appear more plausible if one considers the probabilistic bias affecting convergence [19], bearing in mind that we have demonstrated that biological evolution operating in algorithmic space can better explain some phenomenology related to natural selection [11].…”

Causal deconvolution by algorithmic generative models

“…In another category are methods introduced by Hernández-Orallo et al and their computational measures of information gain and reinforcement in inference processes [32,31], alternatives to ours. One novelty in our approach based on the concept of algorithmic information dynamics [57,8] is the precomputation of a very large set of small models able to explain small pieces of data, which assembled together in sequence, can build a full model of larger data. The method's precomputation allows practical applications in linear time by implementing a look-up table [37,46], which combined with classical information theory provides key hints on the algorithmically random versus non-random nature of data.…”