Evolution and robustness are thought to be intimately connected. Are solutions to optimization problems produced by evolutionary algorithms more robust to mutation than those produced by other classes of search algorithms? We explore this question in a model system based on bivariate real functions. Bivariate real functions serve as a well understood model system that is easy to visualize. Both the number and robustness of optimal solutions found in multiple trials with several typical optimization algorithms were compared. In the majority of the function landscapes explored the tournament selection algorithm found optimal solutions which were significantly more robust to mutation than those discovered by the other algorithms.