SAGE

Speed of Adaptation in Population Genetics and Evolutionary Computation

 Coordinatore THE UNIVERSITY OF NOTTINGHAM 

 Organization address address: King's Meadow Campus, Lenton Lane
city: Nottingham

contact info
Titolo: Mr.
Nome: Paul
Cognome: Cartledge
Email: send email
Telefono: 44115951679

 Nazionalità Coordinatore United Kingdom [UK]
 Totale costo 2˙041˙400 €
 EC contributo 1˙578˙080 €
 Programma FP7-ICT
Specific Programme "Cooperation": Information and communication technologies
 Code Call FP7-ICT-2013-C
 Funding Scheme CP
 Anno di inizio 2014
 Periodo (anno-mese-giorno) 2014-01-01   -   2016-12-31

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    THE UNIVERSITY OF NOTTINGHAM

 Organization address address: King's Meadow Campus, Lenton Lane
city: Nottingham

contact info
Titolo: Mr.
Nome: Paul
Cognome: Cartledge
Email: send email
Telefono: 44115951679

UK (Nottingham) coordinator 0.00
2    FRIEDRICH-SCHILLER-UNIVERSITAT JENA

 Organization address address: FUERSTENGRABEN
city: JENA

contact info
Titolo: Prof.
Nome: Tobias
Cognome: Friedrich
Email: send email
Telefono: +49 3641946321
Fax: +49 3641946322

DE (JENA) participant 0.00
3    Institute of Science and Technology Austria

 Organization address address: Am Campus
city: Klosterneuburg

contact info
Titolo: Ms.
Nome: Carla
Cognome: Mazuheli-Chibidziura
Email: send email
Telefono: +43 224390001038
Fax: +43 224390002000

AT (Klosterneuburg) participant 0.00
4    THE UNIVERSITY OF SHEFFIELD

 Organization address address: FIRTH COURT WESTERN BANK
city: SHEFFIELD

contact info
Titolo: Ms.
Nome: Joanne
Cognome: Watson
Email: send email
Telefono: +44 114 222 4754
Fax: +44 114 222 1455

UK (SHEFFIELD) participant 0.00

Mappa


 Word cloud

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quantitative    evolutionary    algorithms    efficiency    unified    computation    optimisation    predictions    artificial    population    tuning    evolution    biology    biological    genetics    speed    theory   

 Obiettivo del progetto (Objective)

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.

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