In this paper, a neural network is used to implement an optimized objective function for a genetic algorithm (GA) for application on array antenna design optimization. Traditional GAs are inefficient because a large amount of data that 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 GA's objective function via the neural network. The neural network learns the optimal weights of the objective function by identifying trends and optimizing weights depending on the knowledge that it accumulates in-situ. The NNEGA is successfully applied to challenging array antenna design problems. This use of neural network to optimize a multi-objective function for the GA is a new idea that is different from other hybridization of GA and NN.
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