# What is the difference between Stochastic Hill Climbing and Simulated Annealing?

I am trying to use a simulated annealing algorithm to tune the weights of a neural network. I was going to use pybrain's optimization.StochasticHillClimber class to implement simulated annealing, but I realize that Stochastic Hill Climbing and Simulated Annealing may be different algorithms.

From the code of the StochasticHillClimber class, it looks like it does not decrease the Temperature T variable each iteration. Is this the only difference between stochastic hill climbing and simulated annealing?

• OMSCS CS7641? :) I thought the same thing, it seems like the difference is in the temperature dropping. Scipy has an implementation of simulated annealing, but it was removed in 0.16.0 – hankd Mar 11 '16 at 5:05

The difference between simulated annealing and stochastic hill climbing is just that the Temperature T each iteration in simulated annealing decreases, whereas in pybrain's current (version 0.3) optimization.StochasticHillClimber implementation, the Temperature stays constant.