Automatic design of Neural Networks with L-Systems and genetic algorithms - A biologically inspired methodology

In this paper we introduce a biologically plausible methodology capable to automatically generate Artificial Neural Networks (ANNs) with optimum number of neurons and adequate connection topology. In order to do this, three biological metaphors were used: Genetic Algorithms (GA), Lindenmayer Systems (L-Systems) and ANNs. The methodology tries to mimic the natural process of nervous system growing and evolution, using L-Systems as a recipe for development of the neurons and its connections and the GA to evolve and optimize the nervous system architecture suited for an specific task. The technique was tested on three well known simple problems, where recurrent networks topologies must be evolved. A more complex problem, involving time series learning was also proposed for application. The experiments results shows that our proposal is very promising and can generate appropriate neural networks architectures with an optimal number of neurons and connections, good generalization capacity, smaller error and large noise tolerance.