Natural selection is the central concept in biology, and selection is widely used to solve hard computational problems. This proposal aims to deepen our understanding of selection, in both evolutionary biology and evolutionary computation, and to help bring these fields together. On the one hand, population genetics can show how to optimise genetic algorithms, and can inspire new algorithms. On the other, the central problem in evolutionary computation is to optimise the "evolvability" of the algorithms - an issue that has only recently become prominent in biology. Also, computer science may give biologists insight into how selection can concentrate information from the environment into complex organisms, and how organisms can develop under the guidance of their surprisingly small genomes. This project will focus on the factors that limit natural selection: lack of recombination, interaction between genes, and spatial subdivision. Novel techniques will be applied: multilocus algebra, branching processes, an analogy with statistical mechanics, and a new model for population structure. This analysis will be applied to biological and computational problems in parallel, focusing on how recombination aids selection; how epistasis may evolve to facilitate adaptation; and how selection acts in populations subject to frequent extinction and recolonisation. A new optimisation algorithm will be developed, which is amenable to mathematical analysis. Some components are straightforward, whilst others need new ideas, drawn from the interface between population genetics and computer science. Perhaps most challenging will be to understand how selection can so effectively gather information from the environment, so as to construct complex organisms.
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