Cartesian Genetic Programming (CGP) literature repeatedly reports that crossover operators hinder CGP search compared to a 1 + λ strategy based on mutation only. Though there have been efforts in making CGP crossover operators work, the literature is relatively evasive on why the phenomenon is observed at all. This contrasts with what happens in Linear Genetic Programming (LGP), where we know that crossover works well. While both CGP and LGP individuals can be represented as directed acyclic graphs (DAGs), changing a single connection gene in a CGP individual can drastically alter the activeness of nodes in the entire graph, as opposed to LGP where crossover changes are much more beneficial. In this contribution, we demonstrate the phenomenon and show that LGP evolution produces children that are far more similar to their parents than in CGP. This lets us propose that the design of LGP, namely the inclusion of steady-state memory registers and program size regulation, serves to protect highfitness substructures from perturbation in a way that is not provided for in CGP.