%0 Journal Article %1 Koza1991 %A Koza, J.R. %A Rice, J.P. %D 1991 %J Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on %K LISP, adder, algorithms, architecture, arithmetic, connectivity, digital elements expressions, genetic layers, net netsLISP neural one-bit performance, processing programming, symbolic weights, %P 397-404 vol.2 %R 10.1109/IJCNN.1991.155366 %T Genetic generation of both the weights and architecture for a neural network %V ii %X Shows how to find both the weights and architecture for a neural network, including the number of layers, the number of processing elements per layer, and the connectivity between processing elements. This is accomplished by using a recently developed extension to the genetic algorithm which genetically breeds a population of LISP symbolic expressions of varying size and shape until the desired performance by the network is successfully evolved. The novel `genetic programming' paradigm is applied to the problem of generating a neural network for a one-bit adder

@article{Koza1991, abstract = {Shows how to find both the weights and architecture for a neural network, including the number of layers, the number of processing elements per layer, and the connectivity between processing elements. This is accomplished by using a recently developed extension to the genetic algorithm which genetically breeds a population of LISP symbolic expressions of varying size and shape until the desired performance by the network is successfully evolved. The novel `genetic programming' paradigm is applied to the problem of generating a neural network for a one-bit adder}, added-at = {2009-09-12T19:19:34.000+0200}, author = {Koza, J.R. and Rice, J.P.}, biburl = {https://www.bibsonomy.org/bibtex/2b55c65b04eef027e76ce6fd6e69463ba/mozaher}, doi = {10.1109/IJCNN.1991.155366}, file = {:Koza1991.pdf:PDF}, interhash = {bf8c9814cc4972e15ce04693e31a1aba}, intrahash = {b55c65b04eef027e76ce6fd6e69463ba}, journal = {Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on}, keywords = {LISP, adder, algorithms, architecture, arithmetic, connectivity, digital elements expressions, genetic layers, net netsLISP neural one-bit performance, processing programming, symbolic weights,}, month = {8-14 Jul}, owner = {Mozaher}, pages = {397-404 vol.2}, timestamp = {2009-09-12T19:19:40.000+0200}, title = {Genetic generation of both the weights and architecture for a neural network}, volume = {ii}, year = 1991 }