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Also, I found these algorithms could always go more and more meta. For instance, in some situations a large "clone fraction" was optimal, or large "mutation rat

Also, I found these algorithms could always go more and more meta. For instance, in some situations a large "clone fraction" was optimal, or large "mutation rate", whilst in other cases it brought chaos and degraded the population. Successful parameters could also become more immune to mutation with time, a nice improvement along with "speciation" you cited.

Xcelerate 13y ago
> Also, I found these algorithms could always go more and more meta. Haha, I've had this same thought before. I wrote a genetic algorithm to work on the sphere packing problem (in a periodic box) and I kept thinking about how I could mutate the settings within the program and optimize them, and then mutate the rate at which those settings themselves changed, and so on. I wonder if there's an optimal point to stop being "meta"?
- gknoy 13y ago
  We could probably find it with some kind of Monte-Carlo technique...
- darkmighty 13y ago
  There are information theoretic aspects behind this. You're essentially searching the design space for the fittest and want a fast way to get your answer -- this 'fastest way' is related to the way that 'grants' maximum information. I say this because this reminded me of a wonderful (and acessible) discussion I once glanced over on the following book: http://www.cs.toronto.edu/~mackay/itprnn/book.pdf http://www.cs.toronto.edu/~mackay/itprnn/book.pdf (Page 269, pdf 281) -- it offers answers to this sort of question :)
- randomsearch 13y ago
  You're looking for "hyper-heuristics." So called, because unfortunately GAs etc. had already taken the name "meta-heuristics".
- tripzilch 13y ago
  > I wonder if there's an optimal point to stop being "meta"? Perhaps this will help your thinking in the right direction: http://en.wikipedia.org/wiki/No_free_lunch_in_search_and_optimization http://en.wikipedia.org/wiki/No_free_lunch_in_search_and_opt...
rmc 13y ago
Successful parameters could also become more immune to mutation with time I believe this happens in nature. DNA has lots of error correcting stuff to reduce errors.

Xcelerate 13y ago

> Also, I found these algorithms could always go more and more meta. Haha, I've had this same thought before. I wrote a genetic algorithm to work on the sphere packing problem (in a periodic box) and I kept thinking about how I could mutate the settings within the program and optimize them, and then mutate the rate at which those settings themselves changed, and so on. I wonder if there's an optimal point to stop being "meta"?

gknoy 13y ago
We could probably find it with some kind of Monte-Carlo technique...
darkmighty 13y ago
There are information theoretic aspects behind this. You're essentially searching the design space for the fittest and want a fast way to get your answer -- this 'fastest way' is related to the way that 'grants' maximum information. I say this because this reminded me of a wonderful (and acessible) discussion I once glanced over on the following book: http://www.cs.toronto.edu/~mackay/itprnn/book.pdf http://www.cs.toronto.edu/~mackay/itprnn/book.pdf (Page 269, pdf 281) -- it offers answers to this sort of question :)
randomsearch 13y ago
You're looking for "hyper-heuristics." So called, because unfortunately GAs etc. had already taken the name "meta-heuristics".
tripzilch 13y ago
> I wonder if there's an optimal point to stop being "meta"? Perhaps this will help your thinking in the right direction: http://en.wikipedia.org/wiki/No_free_lunch_in_search_and_optimization http://en.wikipedia.org/wiki/No_free_lunch_in_search_and_opt...

rmc 13y ago

Successful parameters could also become more immune to mutation with time I believe this happens in nature. DNA has lots of error correcting stuff to reduce errors.