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 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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