In-situ optimization of cost function for genetic algorithm using neural networks applied to antenna design

In this paper, a neural network is used to implement a generalized cost function for a genetic algorithm (GA). Traditional GAs are inefficient because a large amount of data which describes the problem space is discarded after each generation. Using the neural network enhanced genetic algorithm (NNEGA), this redundant information is fed back into the GAs´ cost function via the neural network. The neural network learns the optimal weights of the cost function by identifying trends and compromising weights depending on the knowledge that it accumulates in-situ. The NNEGA is applied to an array antenna design problem for verification. To ensure convergence, the output of the neural network is only fed back to the cost function after a certain number of generations