Using evolution to solve future design problems

   
   
  Evolution
07 Mar 2014 16:16:23.540

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An international study will increase understanding of how evolution works – and how we can mimic it to tackle a range of 21st century challenges.

Project SAGE (Speed of Adaptation in Population Genetics and Evolutionary Computation) brings together researchers from The University of Nottingham, The University of Sheffield, Friedrich-Schiller-Universität Jena and IST Austria, and has been awarded €2m by the European Union to carry out its research.

The project, coordinated by Dr Per Kristian Lehre from the ASAP Reserach Group in the School of Computer Science at The University of Nottingham, brings together two research fields that study evolution - population genetics and evolutionary computation – to create one unified theory that is capable of explaining how quickly complex adaptations can evolve.

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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 optimisation 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 optimisation 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 form the core of our understanding of biological evolution by formalising it mathematically. Evolutionary computation has independently developed tools to understand how quickly evolutionary algorithms find high-quality solutions for optimisation.

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.

Dr Lehre says: “We envisage that this theory will deepen our understanding of evolution. By bringing together these two research fields, and creating one unified theory, scientists will be able to make long-term predictions about the efficiency of evolution in settings that are highly relevant for both fields and related sciences.”

For more information about SAGE please visit http://www.project-sage.eu  

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Notes to editors: The University of Nottinghamhas 43,000 students and is ‘the nearest Britain has to a truly global university, with campuses in China and Malaysia modelled on a headquarters that is among the most attractive in Britain’ (Times Good University Guide 2014). It is also the most popular university among graduate employers, the world’s greenest university, and winner of the Times Higher Education Award for ‘Outstanding Contribution to Sustainable Development’. It is ranked in the World's Top 75 universities by the QS World University Rankings.

Impact: The Nottingham Campaign, its biggest-ever fundraising campaign, is delivering the University’s vision to change lives, tackle global issues and shape the future. More news…

Story credits

More information is available from Dr Per Kristian Lehre on +44 (0) 115 8468 376, PerKristian.Lehre@nottingham.ac.uk
CharlotteAnscombe

Charlotte Anscombe - Media Relations & Campaign Manager

Email: charlotte.anscombe@nottingham.ac.uk  Phone:+44 (0)115 74 84 417 Location: University Park

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