Adaptive system for generating neural networks using genetic algorithms

An adaptive system is described which generates and trains neural networks using genetic algorithms. A genetic algorithm optimizes the network architecture trying to use as few connections as possible. The neurons of the networks generated by this algorithm are not necessarily organized in layers (except input and output). Because of this, classical algorithms for training neural networks can not be used. Therefore a second genetic algorithm is used to optimize the weights for each generated architecture. During simulation it is possible to change the parameters for the genetic algorithms like the mutation probability or the population size, the size of the networks generated as well as the desired size of the input and output layer and even the data used for training the networks. Therefore the system is able to adapt to a changing environment. The system generates C/C++ code for a `recall only' version of the best network found.