Genetic algorithms are basically a guided trial-and-error methodology. The only advantage I can think of for a GA over a exhaustive search is that since GA optimizes a fitness function in steps, you *might* get to an optimal solution faster, because the GA will favor solutions that are incrementally better. An exhaustive search that’s guaranteed to find a solution might go the long way around.

- If “computation resources” means CPU but not memory, then the GA gene pool
*might* have a smaller memory footprint, requiring less memory.
- On the other hand, because of hill climbing, a GA might get stuck on a local maximum & any mutations might not be sufficient to shake it loose.
- Also, the search time for GA grows exponentially with the size of the input, so an exhaustive search may end up being faster after all, depending on the size of the space you’re searching, and if you can constrain the size of the space by excluding possibilities.

…

Lately I’ve been thinking about GAs in terms of “entropy per second” and measuring the progress of my GA as a measure of how many distinct possibilities it runs through per second. Then I can compare that to the total entropy of the problem space. If a brute force search can beat that entropy per second with parallelization or fast processors, then a GA’s “best score” isn’t any better than a discovered “best score”.

But I’ve just been noodling that over; I haven’t actually instrumented a GA that way yet.

(Entropy is `ln(N)`

for N possible states, or `ln(N) - sum(n * ln(n) for all n) / N`

where `n`

is the possible ways to achieve one result out of N possible outcomes.)

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