Perturbation Search

Finally got around to posting a video of my experiment as described by my previous description. It definitely achieves my goals of locating a fit individual faster, but it definitely does not search as far a field as a true GA.

I was thinking about this, and solution spaces that contain many optimum solutions. The approach above simply uses the first agent that has a solution and attempts to improve on the solution. This solution, will be located "nearby" in the search space. Every generation without an improved result increases the search area by increasing the range of perturbed values.

If it goes on long enough, this will simply devolve into a purely random search.

I'm also pondering if I were to extend the random search; instead of using the first found individual to lookup a best result, we can continually use the random search, and every individual found put through the perturbation search. That could help locating another optimum solution in a different space.

Interesting questions, however I want to explore other such stochastic search methods. Onwards!