3 ms·
Up-voting because even though genetic algorithms seem inefficient compared to back-propagation with gradient descent, it still is an elegant optimization heuris
by hcarvalhoalves 9y ago
Up-voting because even though genetic algorithms seem inefficient compared to back-propagation with gradient descent, it still is an elegant optimization heuristic for many classes of practical problems, and often easier to implement compared to NNs if you're trying to apply on a novel domain.
- sacert 9y agoVery true - I had some time at work and remembered how big genetic algorithms were a couple years ago so I decided to get my feet wet in the subject.
- hcarvalhoalves 9y agoA common use case is solving design constraints: https://www.google.com/?q=genetic+algorithm+placement+format:pdf https://www.google.com/?q=genetic+algorithm+placement+format...
- xoroshiro 9y ago>Easier to implement. I heard about it first while reviewing literature for some of my operations research classes. They were supposedly good heuristics for hard problems like scheduling and facility layouts, but of course I didn't really get in to that for those introductory classes. I wonder if it's really less scary (harder) than I originally thought? I can't seem to find good resources on them online though, as all the moocs and resources are still raving on about machine learning and data science.
- nurikolan 9y agoGAs are also more relevant to problems that cannot be represented in continuous vector space, i.e. where the problem is symbolic and not real-numbered.