874 search results - page 90 / 175 » Iterative Learning Control - Monotonicity and Optimization |
GECCO
2008
Springer
14 years 11 months ago
2008
Springer
For artificial entities to achieve high degrees of autonomy they will need to display appropriate adaptability. In this sense adaptability includes representational flexibility gu...
SAB
2010
Springer
14 years 8 months ago
2010
Springer
Biological brains can adapt and learn from past experience. In neuroevolution, i.e. evolving artificial neural networks (ANNs), one way that agents controlled by ANNs can evolve t...
GECCO
2009
Springer
15 years 2 months ago
2009
Springer
Evolution of neural networks, as implemented in NEAT, has proven itself successful on a variety of low-level control problems such as pole balancing and vehicle control. Nonethele...
NN
2008
Springer
14 years 10 months ago
2008
Springer
Optimally designing the location of training input points (active learning) and choosing the best model (model selection) are two important components of supervised learning and h...
GECCO
2006
Springer
15 years 1 months ago
2006
Springer
This study aims to design a new co-evolution algorithm, Mixture Co-evolution which enables modeling of integration and composition of direct co-evolution and indirect coevolution....