Neural networks with multiple general neuron models: a hybrid computational intelligence approach using Genetic Programming.
Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of neural network (NN-GP) is presented in which: (i) each neuron may have its own neuron model in the form of a general function, (ii) any layout (i.e network interconnection) is possible, and (iii) no bias nodes or weights are associated to the connections, neurons or layers. The general functions associated to a neuron are learned by searching a function space. They are not provided a priori, but are rather built as part of an Evolutionary Computation process based on Genetic Programming. The resulting network solutions are evaluated based on a fitness measure, which may, for example, be based on classification or regression errors. Two real-world examples are presented to illustrate the promising behaviour on classification problems via construction of a low-dimensional representation of a high-dimensional parameter space associated to the set of all network solutions.
Barton
Alan J
AJ
Knowledge Discovery Group, Institute for Information Technology, National Research Council Canada, Ottawa, Ontario, Canada. alan.barton@nrc-cnrc.gc.ca
Valdés
Julio J
JJ
Orchard
Robert
R
D000465:Algorithms; D001110:Arctic Regions; D001185:Artificial Intelligence; D016208:Databases, Factual; D005843:Geography; D055593:Geological Phenomena; D046448:Ice Cover / Q000737:chemistry; D008957:Models, Genetic; D016571:Neural Networks, Computer; D012984:Software