HyperNEAT represents a class of neuroevolutionary algorithms that captures some of the power of natural development with a ionally efficient high-level abstraction of development....
Jeff Clune, Benjamin E. Beckmann, Philip K. McKinl...
The Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) approach demonstrated that the pattern of weights across the connectivity of an artificial neural network ...
The emergence of multicore processors has increased the need for simple parallel programming models usable by nonexperts. The ability to specify subparts of a bigger data structur...
The efficacy of mutation analysis depends heavily on its capability to mutate programs in such a way that they remain executable and exhibit deviating behaviour. Whereas the forme...
Reuse distance analysis has been proved promising in evaluating and predicting data locality for programs written in Fortran or C/C++. But its effect has not been examined for ap...