GP can take the structure of the space into account (you just need to take it into account into your mutation and crossover functions) and algorithms are typica

GP can take the structure of the space into account (you just need to take it into account into your mutation and crossover functions) and algorithms are typically compared on enough runs to provide statistically significant results (29 runs might be far from enough if you have high variance...) but you don't need reproducibility when solving an optimisation problem in the real world, you just want to get a good result in a reasonable time. The fact that you can use GP to optimize about anything (both parameters and structures) makes it a good first approach (where it fails is that it tends to be slow and have way too much parameters for a casual user). (I did think that your paper is interesting in that it invites us to think about non-GP based approach to symbolic regression)