Fractal optimising, a first paper

The fractal "family" of algorithms actually started as a very naïve optimising algorithm: after all, intelligence is just about maximising a certain "utility function", so they are quite related.

Once the fractal AI was done, the optimisation facet was again re-visited with a much more promissing results to later abandon it again.

And finally, with the help of our friend José María Amigó from the Miguel Hernandez University, we wrote an article about this fractal algorithm we named "GAS" (namely for "General Algorithmic Search" but it was actually for the capitals on our names, Guillem, Amigó and Sergio) and compared it against other similar ones out there (Basin hopping, Descent evolution and Cuckoo search).

Folding proteins with a fractal

Yesterday I posted about the fractal algorithm adapted to function optimising, the test I showed was nice, but dealing with a 2D function is not so great if you plan to go big.

After that Guillem forgot to go home and stayed coding a little more at the office. This morning the algorithm was able to load test proteins and try to fold them by minimising the Lennard-Jones potential of the cluster. He sleept in the sofa!

Here you see it folding a 5 atoms "protein":

Serious fractal optimizing

Guillem Duran, my friend and college from twitter, is now full-time at work with me at converting the "one way" fractal algorithm used in my latest AI into a "serious" general optimising algorithm in python: given a function, find its global (as opposed to local) maximum -or minimum- value it can get on its entire domain -the points on the state space where the function is defined.

Last week we finished the conversion, so we are just starting to benchmark it against other "state of the art" similar algorithms, but to have a first view of the you have a preliminar video here:

Benchmarking a fractal algorithm

An algorithm is only "promising" until you perform some benchmarks against others similar state-of-the-art algorithms that are aimed at the same kind of problems.

Benchmarking a general AI algorithm is not easy as it is supposed to generate "intelligent behaviour" and it is not quite clearly defined, but I also develop some other fractal algorithms aimed at finding the global minimum of a real function, or "optimising" functions, and in this case, the algorithm is easily comparable with others, so here we go.

Fractal function optimization

I planed to upload only one video per day until I had no more things to show you about those fractals, but I can not start this serie of posts without showing you one of my favourites: a fractal whos growth laws were designed to make it "sniff" towards the function's global maximum, go there, and then start a deep scan to find a better place.

A "maximizing plant" if you prefer.

Introducing fractal intelligence

Hi again dear -and rare- reader.

I am back writting after some months without activity, and it was not because I was busy writing a paper about the entropic intelligence (it is still unfinished), it was because I have been working hard on a "fractal" version of the intelligence engine.

Fractals are for algortihms what bluetooth is (was?) for devices: everything works better and faster. The dark side of fractals are they are hard to define, too caothic, at least if you want to make real calculations with them (instead of generating nice fractal images).