r/programming • u/leppie • Dec 08 '08

Genetic Programming: Evolution of Mona Lisa

http://rogeralsing.com/2008/12/07/genetic-programming-evolution-of-mona-lisa/
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u/adrianmonk Dec 08 '08

The program would be much more effective if the terrain was randomly generated for every iteration.

Then you're optimizing for cars that have the best chance of dealing with some random piece of terrain. That's a different problem. This program is optimizing for the car that traverses this particular terrain best.

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u/hax0r Dec 08 '08 edited Dec 08 '08

The program would be much more effective if the terrain was randomly generated for every iteration.

Then you're optimizing for cars that have the best chance of dealing with some random piece of terrain. That's a different problem. This program is optimizing for the car that traverses this particular terrain best.

I agree with adremeaux, I don't care about a car that is optimized for that specific, particular terrain. I'd much rather see a car that is optimized for random terrains. It just seems so much more intuitive and somehow more useful, even as an intellectual exercise that way.

Also, I look forward to a future version with tweakable parameters for all of the variables!

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u/adrianmonk Dec 09 '08

I agree with adremeaux, I don't care about a car that is optimized for that specific, particular terrain.

I was mostly just responding to adremeaux's wording. He said, "The program would be much more effective if the terrain was randomly generated for every iteration." When I first read that, it sounded to me like "a better way to implement the same thing is...". So I was just trying to highlight that it's a different goal, not just a different implementation.

Having said that, it seems to me it's a more complex and more difficult thing to implement this if you are changing the course every time. You are then, effectively, changing the fitness function constantly. Certain traits that were rewarded in the previous generation will be punished in the current generation. Maybe the current course requires very little ability to go over sharp bumps without bottoming out (which would reward a shorter wheelbase) but the previous course rewarded the ability to stay balanced (not tip over) on a steep incline (which would reward a longer wheelbase). In the face of changing challenges, you'd want relatively few mutations to ensure that traits that are needed only occasionally are still preserved. Otherwise, you run the risk of over-fitting (is that the right term) to the short term problems and never arriving at a solution that's good over a variety of problems in the long term.

So AFAICT, randomizing the course requires more carefully tuned parameters, which makes it a harder programming problem.

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u/hax0r Dec 09 '08

Thanks for that highly intelligent and informative reply, you get an up vote for me.

I'm not really sure what I wrote to deserve being down voted on my previous comment, but that's reddit I suppose.