Presenting the training patterns, which are mapping to specify features respectively, fulfills supervised learning of Neocognitron. However, the training patterns and many parameters are designed empirically and set manually in Fukushima's Neocognitron. In this paper, we use Genetic Algorithms (GAs) to tune the parameters of Neocognitron and search its reasonable training pattern sets. First, the correlation amongst the training patterns is considered as a critical factor affecting Neocognitron's performance, but it is ignored in the design of the original Neocognitron. Then, a GA-based supervised learning of the Neocognitron is proposed to tune the parameters and search training patterns. The results prove that the performance of a Neocognition is sensitive to its training patterns, selectivity and receptive fields, and can be improved by this supervised learning based on Gas and the correlation analysis.