Simulated Annealing (SA) is a very basic, yet very useful optimization technique. In principle, it’s a modification of what’s sometimes called a “hill climbing” algorithm.
Let’s look at a practical example to explain what hill climbing is, and what SA addresses. Imagine you’re in a 1-dimensional landscape and you want to get to the highest possible point. Further, a crazed optimization expert has blindfolded you so you can’t see anything; all you can do is randomly try to go either left or right, by tapping your foot to feel if a step in that direction is higher than where you’re currently standing. If it is, you take that step, and repeat.
A while ago, I did a post on beating OpenAI games using neuroevolution (NE). Go read that if you’re interested, but here’s the gist: a typical strategy for training an agent to beat those games is to have a neural network (NN) play the games a bunch, and then improve the weights of the NN using a reinforcement learning algorithm that uses gradient descent (GD), and it of course works pretty well.
However, an alternative to those methods is to use a gradient free method (which I’ll call “GD-free”), like I did in that post: you try a bunch of random changes to the NN’s weights, and only keep the resulting NNs that play the game well. That’s the “evolutionary” aspect of it, and using methods like that to create NNs is often called “neuroevolution” (NE).
Similar to…most of? my ideas, I don’t remember why I thought of this. I think after I made the reinforcement learning robot, I was on a robot kick, and came up with this. Hexapods are of course a robot classic, but I don’t think I had ever seen a centipede robot.
Why a centipede? Well… I can make up a few “practical” reasons: because of its length, it could potentially bridge gaps, or bend “upwards” to have height, or possibly even climb. But the real reason is because they haven’t been done that much and I thought it would be cool, funny, and creepy.