The authors study on the hand gesture discernment based on the surface electromyogram of forearm. In order to discern finger shapes of the rock-paper-scissors, genetic programming technique is applied to establish the optimum classification algorithm of hand gestures by composing of arithmetic functions. We measur myoelectric potential signals of forearm related to rock-paper-scissors, and applies them to genetic evolution of hand gesture classification. We also evaluated the effects of the target number of nodes, crossover rate, mutation rate of GP parameters. Realtime hand gesture identification experiments are carried out and the typical hand gestures are actually distinguished in accuracy of 99%.