Fidelity drive: A mechanism for chaperone proteins to maintain stable mutation rates in prokaryotes over evolutionary time

Xue, Julian Z.; Kaznatcheev, Artem; Costopoulos, André; Guichard, Frédéric

doi:10.1016/j.jtbi.2014.09.017

Cited by 2 publications

(1 citation statement)

References 28 publications

(44 reference statements)

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“…We study how mutation rates can evolve within a non-elitist population, where the mutation rate of each individual is encoded by its own genome. The rate at which the mutation rate mutates is specified by a strategy parameter p. In endogenous control, the strategy parameter is itself evolved [1,13]. Here, we consider exogenous control of the strategy parameter p, where the value of the parameter is fixed before the run.…”

Section: Introductionmentioning

confidence: 99%

Self-adaptation of Mutation Rates in Non-elitist Populations

Dang

Lehre

2016

Lecture Notes in Computer Science

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Abstract. The runtime of evolutionary algorithms (EAs) depends critically on their parameter settings, which are often problem-specific. Automated schemes for parameter tuning have been developed to alleviate the high costs of manual parameter tuning. Experimental results indicate that self-adaptation, where parameter settings are encoded in the genomes of individuals, can be effective in continuous optimisation. However, results in discrete optimisation have been less conclusive. Furthermore, a rigorous runtime analysis that explains how self-adaptation can lead to asymptotic speedups has been missing. This paper provides the first such analysis for discrete, population-based EAs. We apply levelbased analysis to show how a self-adaptive EA is capable of fine-tuning its mutation rate, leading to exponential speedups over EAs using fixed mutation rates.

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Section: Introductionmentioning

confidence: 99%