This work will use genetic algorithms to solve two classes of optimization problems related to neural networks: (1) the optimization of weights in recurrent and feed forward neural networks and (2) the optimization of the connectivity of neural networks. To establish a challenging criteria for measuring success, it will build and test large networks for radar signal pulse detection. The use of genetic algorithms for defining neural network connectivity has emerged as an area of research only in the last year. A genetic algorithm has been used to fefine network connectivities for small problems, producing nets which learn much faster than standard "fully-connected" nets and which are suprisingly consistent in the number of data presentations required for learning. This work will extend the current approach by combining the genetic algorithm code for weight optimization with a variation on our current approach to optimizing the connectivity. This combination should allow optimization of connectivity and weights simultaneously on large problems. Finally, the successful optimization of feed forward networks using genetic algorithms is significant because these algorithms make no assumptions about how the weights (parameters) that the genetic algorithm is asked to evaluate are actually used. It should be possible to applied the genetic algorithms directly to recurrent network optimization problems which tend to have restrictive storage and computational requirements when trained with true gradient descent learning techniques.