Natural selection is the central concept in biology, and selection is widely used to solve hard computational problems. The SAGE project 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 optimize genetic algorithms, and can inspire new algorithms. On the other, the central problem in evolutionary computation is to optimize 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 focuses on the factors that limit natural selection: lack of recombination, interaction between genes, and spatial subdivision. We use a range of techniques to study these factors, including multilocus algebra, branching processes, analogies with statistical mechanics, and a new model for population structure

We apply this analysis 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 re-colonization. We are developing a new optimization algorithm which will be 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 is understanding how selection can so effectively gather information from the environment, so as to construct complex organisms.

Principal Investigator: Prof. Nick Barton

Team members:

Project reference: 250152

Project acronym: SelectionInformation

EU contribution: EUR 1975640

Project programme: FP7 Ideas

Contract type: ERC Advanced Grant