Abstract
Genetic algorithms (GAS) are used to generate neural networks that
implement Boolean functions. Neural networks both involve an architecture
that is a graph of connections, and a set of weights. The algorithm
that is put forward yields both the architecture and the weights
by using chromosomes that encode an algorithmic description based
upon a cell rewriting grammar. The developmental process interprets
the grammar for l cycles and develops a neural net parametrized by
l. The encoding along with the developmental process have been designed
in order to improve the existing approaches. They implement the following
key-properties. The representation on the chromosome is abstract
and compact. Any chromosome develops a valid phenotype. The developmental
process gives modular and interpretable architectures with a powerful
scalability property. The GA finds a neural net for the 50 inputs
parity function, and for the 40 inputs symmetry function
- 40
- 50
- algorithms,
- boolean
- cell
- connectionism,
- developmental
- encoding,
- function,
- functions,
- genetic
- grammar,
- grammars,
- inputs
- nets,
- networks
- networks,
- neural
- parity
- process,
- property
- rewriting
- scalability
- symmetry
- synthesis,
- systems
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