Mandelbrots
I was bored, so I made this.
Basic introduction to the Mandelbrot set and what this image represents follows.
Rosio Pavoris a blog
Optimal search is easy
Last time we looked at how to solve the eight puzzle using the hill climbing algorithm, which gave us a result much more quickly than a blind depth-first search did, but we wondered if the solution we found was the best we could do, and we asked if there was a way to use heuristics to find not just a solution, but the best solution. Today, we’ll see that there is, and it’s actually really straightforward.
Heuristics are easy
(This post assumes you read the previous one.)
Today we’ll be looking at the hill climbing algorithm, which is just a plain old depth-first search with heuristics added.
“Heuristics” is a fancy word (from the Greek εὑρίσκω, “I discover”) for a very simple concept. In the context of search trees, it simply means that at a every node, you’re going to look at each possible branch, and take the one that looks the most promising first, instead of just one at random. “Most promising” can be a tricky concept, though.0
Our river-crossing example isn’t necessarily the best one to demonstrate the concept, so let’s go with another classic: the 8 puzzle.
