# Random Walk

Random walks are slightly more complex search algorithms than enumeration. Rather putting all of the solutions in some kind of order (which may end up putting good solutions very far away), we keep every parameter (in our case the x and y coordinates) separate, and just adjust each one by a small amount each time. Once again, we keep doing this until we find a solution that's "good enough".

In this example, like with enumeration, the fitness of a particular location is shown by how light the grey colour is. This is randomly generated when the page loads.

Each (x, y) point in the square is a solution, and our goal is to find the best (lightest). We start in the centre and move a set distance at a random angle at each step. Like the enumeration method, I've made the edges wrap around. There's no need for tricks in the way we represent the search space, since we're not imposing a fixed order on the points. This is an advantage of random walks, since they can be dumped on to any problem without any messing around like crafting an exhaustive enumeration. The disadvantage, as you may notice, is that the same (or nearly the same) solutions are checked again and again!