5 search results - page 1 / 1 » A novel generative encoding for exploiting neural network se... |
GECCO
2007
Springer
13 years 10 months ago
2007
Springer
A significant problem for evolving artificial neural networks is that the physical arrangement of sensors and effectors is invisible to the evolutionary algorithm. For example,...
GECCO
2007
Springer
13 years 10 months ago
2007
Springer
Connectivity patterns in biological brains exhibit many repeating motifs. This repetition mirrors inherent geometric regularities in the physical world. For example, stimuli that ...
GECCO
2010
Springer
13 years 9 months ago
2010
Springer
An important goal for the generative and developmental systems (GDS) community is to show that GDS approaches can compete with more mainstream approaches in machine learning (ML)....
GECCO
2010
Springer
13 years 9 months ago
2010
Springer
The Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) approach demonstrated that the pattern of weights across the connectivity of an artificial neural network ...
GECCO
2009
Springer
13 years 9 months ago
2009
Springer
HyperNEAT, a generative encoding for evolving artificial neural networks (ANNs), has the unique and powerful ability to exploit the geometry of a problem (e.g., symmetries) by enc...