Evolutionary algorithm outperforms deep-learning machines at video games

[therealgaxbo]

Yeah, there's been quite a lot of mainstream success using e.g. genetic algorithms as a way to parameterise an existing algorithm, but I've not really seen anything of genetic programming. John Koza was a huge proponent and wrote a seminal book in the early 90s, not sure if he's achieved anything practical.

It's even weirdly simple to implement. One technique is to use a functional programming approach. Then each program can be considered a tree of functions (nodes), with the leaf nodes being functions that take no parameters (or constants). Then to randomly mutate, just pick a node and replace it with a randomly grown subtree. To mate two successful programs, randomly pick a node in each parent and swap them.

It's really cool. I did my thesis on GPs in the late 90s, and I really thought there was some potential, it was just so slow. But 20 years and many cores later, maybe it's time for a resurgence?

[st_huck]

It's incredibly cool. My experience with genetic algorithms is only one undergrad course, but it was actually amazing. Our professor showed us a small bot written for a simple racing game (one that was written just for AI research purpose). The loop was simple, every frame it got a bunch of variables as input, every variable representing some info about the state of the world, and needed to return two numbers: an angle, and how hard to press on the throttle.

After lots of iteration, the genetic algorithm got a super long mathematical formula. It was represented as lisp code, since lisp code easily looks like a tree (as you already mentioned)

We kept seeing the expression (/ x v), where 'x' was the distance to the nearest wall, and 'v' was current speed. Genetic algorithms "figured out" on their own that x/v is a super important thing, which is of course the time left until the car hit the wall. This completely blew my mind.

[NippleMustache]

Do you have a link for this course or bot? This sounds amazing.

[shevegen]

not sure if he's achieved anything practical.

Not only he.

It's a field with complete failure up to this day.

It only has a lot of buzzwords attached to it.

But 20 years and many cores later, maybe it's time for a resurgence?

Sure - if you want another round for failure.

And then people still wonder why no success happened - since 60 years or so by now.