Different evolutionary paths to complexity for small and large populations of digital organisms
Thomas LaBar, Christoph Adami

TL;DR
This study uses digital evolution to show that both small and large populations can evolve greater complexity through different genetic mechanisms, challenging traditional views on the role of population size.
Contribution
It reveals that small populations evolve complexity via genetic drift fixing deleterious mutations, while large populations do so through beneficial mutations, highlighting diverse evolutionary paths.
Findings
Small populations fix slightly deleterious insertions.
Large populations fix rare beneficial insertions.
Both population sizes can evolve increased complexity.
Abstract
A major aim of evolutionary biology is to explain the respective roles of adaptive versus non-adaptive changes in the evolution of complexity. While selection is certainly responsible for the spread and maintenance of complex phenotypes, this does not automatically imply that strong selection enhances the chance for the emergence of novel traits, that is, the origination of complexity. Population size is one parameter that alters the relative importance of adaptive and non-adaptive processes: as population size decreases, selection weakens and genetic drift grows in importance. Because of this relationship, many theories invoke a role for population size in the evolution of complexity. Such theories are difficult to test empirically because of the time required for the evolution of complexity in biological populations. Here, we used digital experimental evolution to test whether large…
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