4 ms·
Other commenters are focusing on the validity of the sim as a scorer, but there's some other issues here. Doesn't it seem to converge a little aggressively? H
by heavenlyhash 10y ago
Other commenters are focusing on the validity of the sim as a scorer, but there's some other issues here.
Doesn't it seem to converge a little aggressively?
How do you feel about the potential bottlenecking pressures of a breeding pool of size... six?
The parameters of the genetic algorithm are so overwhelmingly ready put things permanently askew from random variations in early stages of the trial sequence that I'm not sure the sim really has a significant impact on the result at all. The author even seems to be aware of this, when he admits running it again does not generate similar results, and starting with a single piece of bias in the initial sample causes that result to dominate.
Genetic algorithms aren't something that can be thrown at a problem with zero attention to the parameters. Sorry, no algorithm is that magical.
- Houshalter 10y agoAll optimization algorithms tend to get stuck in local optima. It's great to try to tune the parameters through experimentation, but he only had enough compute time to run the full GA once or twice. Generally I think it's best to use a small population, or even just hillclimbing. But with the smaller population it should run and converge much, much faster. Then you can run it multiple times, and take the best result.