- author = "John R. Koza and James P. Rice",
- title = "Genetic Generation of Both the Weights and Architecture for a Neural Network",
- booktitle = "International Joint Conference on Neural Networks, IJCNN-91",
- year = "1991",
- volume = "II",
- pages = "397--404",
- address = "Washington State Convention and Trade Center, Seattle, WA, USA",
- publisher_address = "1109 Spring Street, Suite 300, Silver Spring, MD 20910, USA",
- month = "8-12 " # jul,
- publisher = "IEEE Computer Society Press",
- keywords = "genetic algorithms, genetic programming, connectionism, one-bit adder, cogann ref, LISP symbolic expressions, connectivity, layers, neural net architecture, neural net weights, ANN, one-bit adder, performance, processing elements, LISP, digital arithmetic, neural nets",
- ISBN = "0-7803-0164-1",
- LCCN = "QA76.87.I57 1991b",
- bibdate = "Wed Jan 15 14:07:16 1997",
- acknowledgement = ack-nhfb,
-
URL = "
http://www.genetic-programming.com/jkpdf/ijcnn1991.pdf",
-
DOI = "
doi:10.1109/IJCNN.1991.155366",
- abstract = "This paper 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 using a recently developed extension to the genetic algorithm which genetically breeds a population of LISP symbolic expressions (S-expressions) of varying size and shape until the desired performance by the network is successfully evolved. The new genetic programming paradigm is applied to the problem of generating a neural network for the one-bit adder.",
-
notes = "Two volumes. IEEE catalog number:
91CH3049-4.
IJCNN-91",
