Paretto frontiers

Last Thursday professor El-Ghazali Talbi from Lille University hold a talk about metaheuristic classification and his unified framework ParadisEO to deal with it on the Elx University Miguel Hernandez CIO, and after the talk (I enjoyed it a lot and learn things a was supposed to already know and some more!) I had a little time to explain my latest fractal methods and show some of the videos I had been producing about them.

There was some debate after the talks about the classification itself and the Pareto frontier way of working on multi objective optimisation problems.

Basically, actual approaches try to maximise all the functions at the same time, if possible, and if not, then obtain the optimum solution for any weighted combination of those functions, so for any possible combination you get a point, and all those points form the "Pareto Frontier" of the problem.

Mixing goals

Next video shows 36 karts driving around with 3 goals each one:

1) Move fast.
2) Pick up the drops.
3) Store the drops.

The internals are easy:

1) When you drive N meters, you score N.
2) When you drive over a drop of size 5, if you have internal storage left (a kart has a capacity of 100) you "upload" the drop and get 5 points into your scoring.
3) When you drive over a "storage" rectangle, if it still has free space, your internal storage is "downloaded" into the storage and you receive scoring for it.

The result is something like a group of ants collecting food and accumulating on some spots: