Channon, A ORCID: https://orcid.org/0000-0001-9224-4931, Aston, E, Day, C, Belavkin, RV and Knight, CG (2011) Critical mutation rate has an exponential dependence on population size. In: ECAL 2011. MIT Press, Cambridge, 117 -124.

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Abstract

Populations of individuals exist in a wide range of sizes, from billions of microorganisms to fewer than ten individuals in some critically endangered species. In any evolutionary system, there is significant evolutionary pressure to evolve sequences that are both fit and robust; at high mutation rates, individuals with greater mutational robustness can outcompete those with higher fitness, a concept that has been referred to as survival-of-the-flattest. Previous studies have not found a relationship between population size and the mutation rate that can be tolerated before fitter individuals are outcompeted by those that have a greater mutational robustness. However, using a genetic algorithm with a simple two-peak fitness landscape, we show that the mutation rates at which the high, narrow peak and the lower, broader peak are lost for increasing population sizes can be approximated by exponential functions. In addition, there is evidence for a continuum of mutation rates representing a transition from survival-of-the-fittest to survival-of-the-flattest and subsequently to the error catastrophe. The effect of population size on the critical mutation rate is shown to be particularly noticeable in small populations. This provides new insight into the factors that can affect survival-of-the-flattest in small populations, and has implications for populations under threat of local extinction.

Item Type: Book Section
Additional Information: This is the accepted author manuscript (AAM). The final published version (version of record) is available online via MIT Press at https://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=12760 - please refer to any applicable terms of use of the publisher.
Uncontrolled Keywords: computer science
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Natural Sciences > School of Computing and Maths
Depositing User: Symplectic
Date Deposited: 11 Nov 2014 10:51
Last Modified: 17 Dec 2018 11:41
URI: http://eprints.keele.ac.uk/id/eprint/44

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