2020
DOI: 10.3390/life10030022
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Evolving Always-Critical Networks

Abstract: Living beings share several common features at the molecular level, but there are very few large-scale “operating principles” which hold for all (or almost all) organisms. However, biology is subject to a deluge of data, and as such, general concepts such as this would be extremely valuable. One interesting candidate is the “criticality” principle, which claims that biological evolution favors those dynamical regimes that are intermediaries between ordered and disordered states (i.e., “at the edge of chaos”). … Show more

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Cited by 3 publications
(6 citation statements)
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“…This observation is compatible with the fact that the asymptotic states of these systems must have a high number of active genes. (This observation is compatible with a remark present in [34], albeit this latter remark provides a lower evidence because of the smaller number of analyzed systems. )…”
Section: Static and Dynamic Characteristics In Evolved Systemssupporting
confidence: 91%
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“…This observation is compatible with the fact that the asymptotic states of these systems must have a high number of active genes. (This observation is compatible with a remark present in [34], albeit this latter remark provides a lower evidence because of the smaller number of analyzed systems. )…”
Section: Static and Dynamic Characteristics In Evolved Systemssupporting
confidence: 91%
“…Given that criticality is supposed to be an important property, it is particularly interesting to consider a modified version of the GA, presented in [34], which preserves the bias of the Boolean functions during inheritance from parents to children. This GA maintains the usual selection procedure (a roulette wheel) and has a slightly modified mutation and crossover.…”
Section: Genetic Algorithmmentioning
confidence: 99%
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“…Random Boolean Networks (RBNs for short) are strongly simplified models of gene regulatory networks (GRNs), proposed by one of us (Kauffman) more than 50 years ago, which have also been widely studied as abstract models of complex systems, thanks to the fact that their dynamical behavior can be tuned from ordered to disordered by modifying a few key parameters. They have also been applied to different biological phenomena, such as, e.g., cell differentiation [1][2][3], as well as to different fields, including robotics [4][5][6], the study of evolutionary processes [7][8][9] and the simulation of social systems [10][11][12][13].…”
Section: Introductionmentioning
confidence: 99%