[tripzilch]

Another reason is for most demos and examples of Genetic Algorithms, that Simulated Annealing is almost always more optimal.

Let me first say that I'm not certain this is the case for the purposes lined out in the article, because I'm not entirely sure what they're trying to do.

Unless you have a crossover operator that really makes sense for your fitness function and problem, a GA is basically nothing but a bunch of SA processes running in parallel. In that case you would prefer SA, because it has less hyperparameters to tune, convergence is better defined, and there are proven methods to tune the hyperparameters.

It's not that hard if you have a working GA optimizer, to test how well it does with SA as a baseline, because the algorithms are fairly similar. Unfortunately not many demos do this baseline comparison and I'm pretty sure most of them won't do significantly better with GAs.