Cargando…
On the Relationships between Generative Encodings, Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks
A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1) the developmental process that links genes to n...
Autores principales: | Tonelli, Paul, Mouret, Jean-Baptiste |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3827315/ https://www.ncbi.nlm.nih.gov/pubmed/24236099 http://dx.doi.org/10.1371/journal.pone.0079138 |
Ejemplares similares
-
Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills
por: Ellefsen, Kai Olav, et al.
Publicado: (2015) -
Evolving the Behavior of Machines: From Micro to Macroevolution
por: Mouret, Jean-Baptiste
Publicado: (2020) -
How adaptive plasticity evolves when selected against
por: Rago, Alfredo, et al.
Publicado: (2019) -
Children’s Evolved Learning Abilities and Their Implications for Education
por: Bjorklund, David F.
Publicado: (2022) -
Artificial Neural Networks with Machine Learning Design for a Polyphasic Encoder
por: Alvarez-Rodríguez, Sergio, et al.
Publicado: (2023)