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Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity
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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6124114/ https://www.ncbi.nlm.nih.gov/pubmed/30225028 http://dx.doi.org/10.1098/rsos.180399 |
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author | Hernández-Orozco, Santiago Kiani, Narsis A. Zenil, Hector |
author_facet | Hernández-Orozco, Santiago Kiani, Narsis A. Zenil, Hector |
author_sort | Hernández-Orozco, Santiago |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6124114 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-61241142018-09-17 Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity Hernández-Orozco, Santiago Kiani, Narsis A. Zenil, Hector R Soc Open Sci Computer Science 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. The Royal Society 2018-08-29 /pmc/articles/PMC6124114/ /pubmed/30225028 http://dx.doi.org/10.1098/rsos.180399 Text en © 2018 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Computer Science Hernández-Orozco, Santiago Kiani, Narsis A. Zenil, Hector Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity |
title | Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity |
title_full | Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity |
title_fullStr | Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity |
title_full_unstemmed | Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity |
title_short | Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity |
title_sort | algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity |
topic | Computer Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6124114/ https://www.ncbi.nlm.nih.gov/pubmed/30225028 http://dx.doi.org/10.1098/rsos.180399 |
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