I am currently experimenting with HyperNEAT to find out how well it responds to faults.  Such as, mechanical faults in a robot’s actuators.  I want to know whether HyperNEAT’s use of geometry will allow it to adapt to faults faster or slower than a regular feed forward backprop neural net.  I am using the CSharp HyperSharpNEAT implementation of HyperNEAT for my experiment.  By conducting this experiment I am, not only learning more about the algorithm, but I am also learning CSharp.

I just recently began to experiment with HyperNEAT (Hypercube based Neuroevolution of Augmenting Topologies). It is a new way to evolve neural networks created by Dr. Stanley. It uses the geometry of the placement of neurons in a neural network as input into a CPPN (Compositional Pattern Producing Network) which is evolved to learn the connection weights between neurons.