Biological evolution has produced an extraordinary diversity of organisms, even the simplest of which is highly adapted, with multiple complex structures. Evolutionary computation has found that many innovative solutions to optimisation and design problems can be achieved by artificial evolution via random variation and selection.
Despite the centrality of evolution to biology and the usefulness of evolutionary algorithms in optimisation, the dynamics of evolution are not well understood. Consequently, population genetics theory can only make quantitative predictions about short-term, simple biological evolution, and the design and parameter tuning of evolutionary algorithms is mostly done ad-hoc in a laborious and cost-intensive process.
Both fields have studied the speed of adaptation independently, and with orthogonal approaches. Our project brings together an interdisciplinary consortium of ambitious researchers from the theory of evolutionary computation and theoretical population genetics to synergise these complementary approaches and to create the foundation of a unified quantitative theory describing the speed of adaptation in both biological and artificial evolution.
The transformative impact of this unified theory will lie in enabling long-term predictions about the efficiency of evolution in settings that are highly relevant for both fields and related sciences. Our approach will reveal how this efficiency is fundamentally determined by evolutionary and environmental parameters. Tuning these parameters will allow researchers from biology and computation to increase the efficiency of evolutionary processes, revolutionising applications ranging from evolutionary algorithms to experimental evolution and synthetic biology.
