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That's what I was thinking too but my AI knowledge is a bit rusty. A genetic algorithm serves as a search algorithm that is finding a solution among a set of ca

That's what I was thinking too but my AI knowledge is a bit rusty. A genetic algorithm serves as a search algorithm that is finding a solution among a set of candidates. For example finding an answer to a sudoku. A GA can also be used to perform optimization. For example arranging the structure of a bridge to maximize the load it can support. The Starcraft build order as defined here is a planning problem, that is a problem whose goal is to find a sequence of action to get to a desired state. There are algorithms more suited than GA because they take advantage of the logical structure of the problem. Do I get that right?

StavrosK 16y ago
I can't tell you if it's "right", as I haven't worked on this particular problem, and ML is hardly black-and-white. GAs might give good results, it's just that there is usually another algorithm that will give better results faster. Again, it might work very well, I just think that some sort of tree-based approach might be faster and more efficient.
- ewjordan 16y ago
  Again, it might work very well, I just think that some sort of tree-based approach might be faster and more efficient. While this is true in general, sometimes the nice thing about genetic methods is that pretty much all you have to do is write the fitness function, cross your fingers that the problem is a good fit for the method and go do some real work on another computer for a while. Oftentimes other methods require more in the way of setup or planning, not the least of which is actually picking an appropriate method and mapping its implementation onto the problem space. Some of the nastiest problems I've ever worked on have gotten that way because I went with a domain specific approach that was "optimized for the problem"; while the end results are usually very good (and, to be fair, run on the computer from zero-to-solved in no time flat), in a couple cases I've gone back and tried the "brute force" genetic programming approach, and though it took quite a bit more computer time, the programming effort was substantially smaller to end up with similarly good results. The real problem, though, is that many of the problems people apply GA/GP to aren't well suited to the genetic approach, usually because the problem itself doesn't lend itself to incrementally improving solutions (problems like function regression can be tricky, because getting close to the correct functional form symbolically usually leaves you very far away from the correct form numerically, and sometimes the correct result is actually surrounded by a "wall" of completely and utterly unfit solutions that's very hard to break through). Genetic methods work well when the fitness landscape has lots of solutions that work fairly well, and quite a few that work great, not when there's literally one needle in some super-multi-dimensional haystack. To be fair, the Starcraft optimization problem is probably somewhere in the middle, there are probably many good solutions to the problem, and many of these will be minor variations on other good solutions, so it's pretty likely that a GA will get to some good ones. But the search space is small enough that you're probably right, a heuristic-guided direct search would be more likely to pick out the best solutions faster.
  - StavrosK 16y ago
    Yep, exactly right. I'm just going by the fact that they probably need to optimize for running time first of all, as this will need to run in real time (or so I assume). GAs would make this very hard, even if the results are good enough.

StavrosK 16y ago

I can't tell you if it's "right", as I haven't worked on this particular problem, and ML is hardly black-and-white. GAs might give good results, it's just that there is usually another algorithm that will give better results faster. Again, it might work very well, I just think that some sort of tree-based approach might be faster and more efficient.

ewjordan 16y ago
Again, it might work very well, I just think that some sort of tree-based approach might be faster and more efficient. While this is true in general, sometimes the nice thing about genetic methods is that pretty much all you have to do is write the fitness function, cross your fingers that the problem is a good fit for the method and go do some real work on another computer for a while. Oftentimes other methods require more in the way of setup or planning, not the least of which is actually picking an appropriate method and mapping its implementation onto the problem space. Some of the nastiest problems I've ever worked on have gotten that way because I went with a domain specific approach that was "optimized for the problem"; while the end results are usually very good (and, to be fair, run on the computer from zero-to-solved in no time flat), in a couple cases I've gone back and tried the "brute force" genetic programming approach, and though it took quite a bit more computer time, the programming effort was substantially smaller to end up with similarly good results. The real problem, though, is that many of the problems people apply GA/GP to aren't well suited to the genetic approach, usually because the problem itself doesn't lend itself to incrementally improving solutions (problems like function regression can be tricky, because getting close to the correct functional form symbolically usually leaves you very far away from the correct form numerically, and sometimes the correct result is actually surrounded by a "wall" of completely and utterly unfit solutions that's very hard to break through). Genetic methods work well when the fitness landscape has lots of solutions that work fairly well, and quite a few that work great, not when there's literally one needle in some super-multi-dimensional haystack. To be fair, the Starcraft optimization problem is probably somewhere in the middle, there are probably many good solutions to the problem, and many of these will be minor variations on other good solutions, so it's pretty likely that a GA will get to some good ones. But the search space is small enough that you're probably right, a heuristic-guided direct search would be more likely to pick out the best solutions faster.
- StavrosK 16y ago
  Yep, exactly right. I'm just going by the fact that they probably need to optimize for running time first of all, as this will need to run in real time (or so I assume). GAs would make this very hard, even if the results are good enough.