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Title
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On the evolution of mutation bias in digital organisms
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Creator
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Rupp, Matthew
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Date
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2011
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Collection
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Electronic Theses & Dissertations
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Description
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Mutation is one of the primary drivers of genetic change. In this work I study mutation biases, which are sets of different genetic-state inflow probabilities. Mutation biases have the potential to change the composition of genomes over time, leading to divergent short- and long-term evolutionary outcomes. I use digital organisms, self-replicating computer programs, to explore whether or not mutation biases are capable of altering the long-term adaptive behavior of populations; whether...
Show moreMutation is one of the primary drivers of genetic change. In this work I study mutation biases, which are sets of different genetic-state inflow probabilities. Mutation biases have the potential to change the composition of genomes over time, leading to divergent short- and long-term evolutionary outcomes. I use digital organisms, self-replicating computer programs, to explore whether or not mutation biases are capable of altering the long-term adaptive behavior of populations; whether mutation biases can be competitive traits; and whether mutation biases can evolve. I find that mutation biases can alter the long-term adaptive behavior of mutation bias-obligate populations in terms of both mean fitness and complex trait evolution. I also find that mutation biases can compete against one another under a variety of conditions, meaning mutation bias can selectable over relatively-short periods of time. The competitive success of a mutation bias does not always depend upon the presence of beneficial mutations, implicating an increase in the probability of neutral mutations as a sufficient mechanism for bias selection. Finally, I demonstrate that by giving organisms a mutable mutation bias allele, populations preferentially evolve to possess specific biases over others. Overall, this work shows that mutation bias can act as a selectable trait, influencing the evolution of populations with regard to both their internal-genetic and external environments.
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Title
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The evolutionary potential of populations on complex fitness landscapes
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Creator
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Bryson, David Michael
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Date
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2012
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Collection
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Electronic Theses & Dissertations
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Description
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Evolution is a highly contingent process, where the quality of the solutions produced is affected by many factors. I explore and describe the contributions of three such aspects that influence overall evolutionary potential: the prior history of a population, the type and frequency of mutations that the organisms are subject to, and the composition of the underlying genetic hardware. I have systematically tested changes to a digital evolution system, Avida, measuring evolutionary potential in...
Show moreEvolution is a highly contingent process, where the quality of the solutions produced is affected by many factors. I explore and describe the contributions of three such aspects that influence overall evolutionary potential: the prior history of a population, the type and frequency of mutations that the organisms are subject to, and the composition of the underlying genetic hardware. I have systematically tested changes to a digital evolution system, Avida, measuring evolutionary potential in seven different computational environments ranging in complexity of the underlying fitness landscapes. I have examined trends and general principles that these measurements demonstrate and used my results to optimize the evolutionary potential of the system, broadly enhancing performance. The results of this work show that history and mutation rate play significant roles in evolutionary potential, but the final fitness levels of populations are remarkably stable to substantial changes in the genetic hardware and a broad range of mutation types.
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