Project SAGE
Speed of Adaptation in Population Genetics and Evolutionary Computation
University of Nottingham



Speed of Adaptation in Population Genetics and Evolutionary Computation (SAGE)

Kick-off meeting

What we do


SAGE brought together an interdisciplinary consortium of researchers from the theory of evolutionary computation and theoretical population genetics to develop a unified theory describing the speed of adaptation in both biological and artificial evolution.

The impact of this unified theory lay in enabling long-term predictions about the efficiency of evolution in settings that are highly relevant for both fields and related sciences.




The project's ultimate goal was to give a unified, quantitative theory for the speed of adaptation that enables inter-disciplinary studies of artificial and biological evolution by combining the complementary approaches from population genetics and evolutionary computation.





We established associated industrial partnerships with selected companies that were keen to exploit fundamental advances from the outcomes of SAGE project in their applications. 

Our researchers spent time at the project partners to transfer our newest innovations in the understanding of evolutionary processes.












The SAGE project completed successfully in December 2016

Prof Tobias Friedrich and his research group have moved this month from the University of Jena to the Hasso Plattner Institute Potsdam. HPI will become a partner of the SAGE project.

Prof Tobias Friedrich co-organised an inter-disciplinary symposium "Symposium Ein halbes Jahrhundert Zickzack mit Darwin: Evolution – Evolutionäre Algorithmen – Artificial Life" in Jena, Germany. Another SAGE member, Dr Dirk Sudholt contributed with a talk about runtime analysis of evolutionary algorithms.

All SAGE members are meeting in Vienna 11-13 August 2014.

The SAGE kick-off meeting took place in Jena, Germany, 3rd to 6th, April 2014.  The meeting was co-located with the annual 8th Workshop on Theory of Randomized Search Heuristics (ThRaSH).


SAGE Project