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
- adder,
- algorithms,
- architecture,
- arithmetic,
- cogann
- connectionism,
- connectivity,
- digital
- elements
- expressions,
- genetic
- imported
- layers,
- lisp,
- net
- netslisp
- neural
- one-bit
- performance,
- processing
- programming,
- ref
- symbolic
- weights,
Users
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