Evolving neural networks through augmenting topologies
Evolutionary Computation
Generating large-scale neural networks through discovering geometric regularities
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolution of multisensory integration in large neural fields
EA'11 Proceedings of the 10th international conference on Artificial Evolution
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We have developed a novel extension of the NEAT neuroevolution method, termed NEATfields, to solve problems with large input and output spaces. NEATfields networks are layered into two-dimensional fields of identical or similar subnetworks with an arbitrary topology. The subnetworks are evolved with genetic operations similar to those used in the NEAT neuroevolution method. We show that information processing within the neural fields can be organized by providing suitable building blocks to evolution. NEATfields can solve a number of visual discrimination tasks and a newly introduced multiple pole balancing task.