Correspondence between neuroevolution and gradient descent.
Nat Commun
; 12(1): 6317, 2021 11 02.
Article
en En
| MEDLINE
| ID: mdl-34728632
ABSTRACT
We show analytically that training a neural network by conditioned stochastic mutation or neuroevolution of its weights is equivalent, in the limit of small mutations, to gradient descent on the loss function in the presence of Gaussian white noise. Averaged over independent realizations of the learning process, neuroevolution is equivalent to gradient descent on the loss function. We use numerical simulation to show that this correspondence can be observed for finite mutations, for shallow and deep neural networks. Our results provide a connection between two families of neural-network training methods that are usually considered to be fundamentally different.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Redes Neurales de la Computación
/
Mutación
Idioma:
En
Revista:
Nat Commun
Asunto de la revista:
BIOLOGIA
/
CIENCIA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos