Sage approach to uncover dynamics of adaptation

Adaptation is a central process in evolution. A new project – initiated in January 2014 and funded with € 2 mio by the European Union – focuses on the speed of adaptation using two different methods of evolutionary biology. A goal of this joint effort is to develop more efficient forms of artificial evolution with a possible impact on various industries.

Project SAGE (Speed of Adaptation in Population Genetics and Evolutionary Computation) – coordinated by Dr. Per Kristian Lehre from the School of Computer Science at The University of Nottingham – brings together two research fields that study evolution: population genetics and evolutionary computation. The idea is to create one unified theory that is capable of explaining how quickly complex adaptations can evolve. Contributor on the part of IST Austria part is postdoc Tiago Paixão from the lab of Prof. Nick Barton, with the Friedrich-Schiller-Universität Jena and the University of Sheffield being the other partners.

Biological evolution has produced an extraordinary diversity of organisms, even the simplest of which is highly adapted, with multiple complex structures. Evolutionary computation mimics this process, creating an artificial evolution to produce innovative solutions to optimization and design problems. These evolutionary algorithms are applied in various industries such as the pharmaceutical industry, the automotive industry, and logistics, to name but a few.

There are countless possibilities for mimicking evolution, however some forms of evolution are more efficient than others in evolving complex adaptations. This efficiency is vital for solving complex large-scale optimization and design problems in a limited amount of time. Yet, identifying efficient forms of evolution is a challenging task, due to a lack of knowledge about how evolution proceeds in these artificial conditions.

SAGE brings together two research fields that have studied the efficiency of evolution, or speed of adaptation, from different angles. Population genetics forms the core of our understanding of biological evolution by formalizing it mathematically. Evolutionary computation has independently developed tools to understand how quickly evolutionary algorithms find high-quality solutions for optimization. Both have studied the speed of adaptation independently, but with different methods and approaches. The SAGE project aims to bring these two fields together to develop a unified theory of the speed of adaptation in natural and artificial evolution. The goal is to combine the advantages of existing research on evolution to better understand the speed of adaptation in evolutionary processes, and to develop more efficient forms of artificial evolution.

For more information visit the SAGE website.