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» Evolving a neural network using dyadic connections
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ECAL
2007
Springer
14 years 1 months ago
Measuring Entropy in Embodied Neural Agents with Homeostasic Units: A Link Between Complexity and Cybernetics
Abstract. We present a model of a recurrent neural network with homeostasic units, embodied in a minimalist articulated agent with a single link and joint. The configuration of th...
Jorge Simão

Publication
234views
13 years 11 months ago
Evolution of Analog Networks using Local String Alignment on Highly Reorganizable Genomes
We introduce and apply a genetic representation for analog electronic circuits based on the association of character strings extracted from the genome with the terminals and param...
Claudio Mattiussi, Dario Floreano
NPL
2000
105views more  NPL 2000»
13 years 7 months ago
Online Interactive Neuro-evolution
In standard neuro-evolution, a population of networks is evolved in a task, and the network that best solves the task is found. This network is then fixed and used to solve future...
Adrian K. Agogino, Kenneth O. Stanley, Risto Miikk...
GECCO
2010
Springer
173views Optimization» more  GECCO 2010»
13 years 11 months ago
The baldwin effect in developing neural networks
The Baldwin Effect is a very plausible, but unproven, biological theory concerning the power of learning to accelerate evolution. Simple computational models in the 1980’s gave...
Keith L. Downing
ESANN
2001
13 years 9 months ago
Optimal transfer function neural networks
Neural networks use neurons of the same type in each layer but such architecture cannot lead to data models of optimal complexity and accuracy. Networks with architectures (number ...
Norbert Jankowski, Wlodzislaw Duch