The author previously reported an experimental result of evolutionary reinforcement learning of binary neural network controllers. In the previous study, the controller was trained by Evolution Strategy. In this study, the author experimentally applies Genetic Algorithm, instead of ES, and the results were compared between GA and ES. In both studies, the same Acrobot control task is utilized, and the same three-layer feedforward neural network is adopted. The difference lies in the training algorithm. The findings from this study are (1) GA trained the controller better than ES (p<.01), (2) increasing the population size, rather than the number of generations, improved performance more in GA (p < .01), and (3) the optimal number of hidden units for the binary MLP was 128 among the choices of 16, 32, 64, 128 and 256, which was consistent with the previous study using ES.