In terms of how they behave, yeah, they're pretty similar—have a population of individuals, evaluate them for fitness, perform copy / mutation / crossover for highly fit individuals, repeat until fitness is high enough, or you get tired of it.

But, the arbitrary nature of the code being executed in GP (vs a fixed set of parameters in a GA), gives the GP system a lot more flexibility in finding an effective solution. The potential solution space is a lot larger.