Ant programming has been proposed as an alternative to Genetic Programming (GP) for the automated production of computer programs. Generalized Ant Programming (GAP) – an automated programming technique derived from principles of swarm intelligence – has shown promise in solving symbolic regression and other hard problems. Enhanced Generalized Ant Programming (EGAP) has improved upon the performance of GAP; however, a comparison with GP has not been performed. This paper compares EGAP and GP on 3 well-known tasks: Quartic symbolic regression, multiplexer and an ant trail problem. When comparing EGAP and GP, GP is found to be statistically superior to EGAP. An analysis of the evolving program populations shows that EGAP suffers from premature diversity loss.