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Machine learning is automated classification, regression, and decision-making. Genetic algorithms don't tend to perform so well in these areas (just as ML is no
by TezlaOil 9y ago
Machine learning is automated classification, regression, and decision-making. Genetic algorithms don't tend to perform so well in these areas (just as ML is not so appropriate for combinatorial optimization).
Instead, genetic methods work well for various messy industrial engineering such as strip packaging design or antenna design. One of my favorite recent results solves a structural engineering problem using grammatical evolution [1].
[1] http://www.sciencedirect.com/science/article/pii/S0926580513002124 http://www.sciencedirect.com/science/article/pii/S0926580513...
- amelius 9y agoPerhaps genetic algorithms could be useful for hyperparameter optimization of deep learning methods? ;)
- bstamour 9y agoIf you have the horsepower to test a population of deep nets, then absolutely.
- R_haterade 9y agoIt's been looked into. I'm really struggling to remember who was doing the looking though, but it exists.
- thearn4 9y agoI remember using GAs to tune SVM kernel hyperparameters a few years ago. They worked well enough, but I mostly did it because I was lazy.