Is There a Difference Between Simulated Annealing and Gradient Decading in Machine Learning?

There are beautiful algorithms emerging in machine learning that nicely interpolate between simulated annealing and gradient descent. What I'm describing is a simple thermodynamic model. Simulated annealing does a bad job of using local gradient information, which is the basis of like back propagation of machine learning. So it's like one of the banes have simulate annealing is what is the pooling schedule and how do you do the innovation? And this is a very different way to answer it. That kind of crosses over into them.