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Abstract. Cryptography depends on two components, an algorithm and a key. Keys are used for encryption of information as well as in other cryptographic schemes such as digital signature and message authentication codes. Neural cryptography is a way to create shared secret key. Key generation in Tree Parity Machine neural network is done by mutual learning. Neural networks receive common inputs to synchronize using a suitable learning rule. Because of this effect neural synchronization can be used to construct a cryptographic key-exchange protocol. Faster synchronization of the neural network has been achieved by generating the optimal weights for the sender and receiver from a genetic process. In this paper the performance of the genetic algorithm has been analysed by varying the neural network and genetic parameters.

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