Hola,
¿eres nuevo aquí?

Regístrate gratis y conecta tu empresa con financiación pública, partners y proyectos.

Tengo cuenta

Regístrate

Ver video

SAGE

Financiado

Cerrado

Speed of Adaptation in Population Genetics and Evolutionary Computation

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 optimi... ver más

31/12/2016

THE UNIVERSITY OF...

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

THE UNIVERSITY OF NOTTINGHAM No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 5 Participantes

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2016-12-31 No tenemos la información de la convocatoria

0% 77% 100%

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

5 Participantes

THE UNIVERSITY OF NOTTINGHAM

0.00€ | Lider

UNI JENA

N.D. | Participante

HPI

N.D. | Participante

Proyectos interesantes

SELECTIONINFORMATION

Data Science

Software

2M€ Cerrado

PID2019-107299GB-I00

Data Science

Software

34K€ Cerrado

GEMS

Data Science

Software

2M€ Cerrado

EvoEvo

Data Science

Software

3M€ Cerrado

TIN2012-30685

Data Science

Software

107K€ Cerrado

OPTINF

Data Science

Software

1M€ Cerrado

Conecta tu I+D

¿Tienes un proyecto y buscas un partner? Gracias a nuestro motor inteligente podemos recomendarte los mejores socios y ponerte en contacto con ellos. Te lo explicamos en este video

Líder del proyecto

THE UNIVERSITY OF NOTTINGHAM No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.<br/>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.<br/>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.<br/>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.

Conecta tu I+D

Entra hoy

¿Olvidé mi contraseña?

Financiación

Empresas

CTIs/Universidades

Proyectos

Investigadores