An evolutionary neural network and a method of generating such a neural network is disclosed. The evolutionary neural network comprises an input set consisting of at least one input neuron, said input neurons being adapted for receiving an input signal form an external system, an output set consisting of at least one output neuron, said output neurons being adapted for producing an output signal for said external system, an internal network composed of a plurality of internal neurons, each internal neuron being adapted for processing a signal received from at least one of said input neurons or other internal neurons and producing a signal for at least one of said output neurons or other internal neurons, and a plurality of synapses constituting connections between said neurons, each of said synapses having a value of strength that can be adjusted by a learning process. Each of said neurons is assigned to a neuron class, the parameter values of which are defined by the genotype of the neural network, and each of said synapses are assigned to a respective synapse class, the parameter values of which are also defined by said genotype of the neural network. At reproduction, the genotype of any new neural network is subject to genetic operations. The evolutionary neural network is associated with a neural space, said neural space comprising a plurality of neural layers. Each neuron is associated with at least one neural layer and described by a set of topographical parameters with respect to said neural space. At least one of said topographical parameters of at least the internal neurons is encoded in the genotype of the evolutionary neural network in a statistical form.
展开▼
