Empirical assessment and comparison of neuro-evolutionary methods for the automatic off-line design of robot swarms
Off-line neuro-evolution produces robot swarms whose good performance in simulation does not often transfer to the real word. With an extensive empirical study, Hasselmann et al. substantiate overfitting as the dominant cause.
Guardado en:
Autores principales: | Ken Hasselmann, Antoine Ligot, Julian Ruddick, Mauro Birattari |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/d3ec80f81eaf4edb9cabe5d2a8fda60d |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Neuro-Swarm heuristic using interior-point algorithm to solve a third kind of multi-singular nonlinear system
por: Zulqurnain Sabir, et al.
Publicado: (2021) -
Path Planning of an autonomous Mobile Robot using Swarm Based Optimization Techniques
por: Ibraheem Kasim Ibraheem, et al.
Publicado: (2017) -
A swarm of autonomous miniature underwater robot drifters for exploring submesoscale ocean dynamics
por: Jules S. Jaffe, et al.
Publicado: (2017) -
Chaotic-based particle swarm optimization algorithm for optimal PID tuning in automatic voltage regulator systems
por: N. Anwar, et al.
Publicado: (2021) -
Numerical Analysis of Electrohydrodynamic Flow in a Circular Cylindrical Conduit by Using Neuro Evolutionary Technique
por: Naveed Ahmad Khan, et al.
Publicado: (2021)