Genetic synthesis of Boolean neural networks with a cell rewriting developmental process

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.<>