No, I am certain you are confusing Genetic Programming [1] for Genetic Algorithms [2]. I am a fan (do a lot with them) of the prior but have never really been impressed by the latter.
Wikipedia says Genetic Programming is a specialization of genetic algorithms where the objects are programs. I think this is the only thing I have ever seen where the specialization turns out to be more general than the parent.
I'd also like to point out that I although all ML learns models, all those I gave can learn stuff as what amounts to a bunch of IF-THEN statements. Well GP need not be so limited. And Random Forests lose the trees in the forest (harder to interpret) but yeah, that's why I formed that particular list.
Then you will know that Genetic Programming can be used for classification, structured learning, symbolic regression and even meta learning [1]. For classification you can build a program that searches for a program that best uses the input to predicts classes. I have done this (there are better heuristics than the old kind that lead to really big dumb programs). Or you can combine boosting with Genetic Programming or you can use them in Learning Classifier Systems. All these lead to classification based on genetic programming.
As an aside, My definition of Machine learning is more inclusive than yours. I mean if you are going to separate out optimization then I guess you don't count stochastic gradient descent or Matrix factorization as part of machine learning? Machine learning is basically the combination of statistics and optimization where you can work with a lot of data and the output of your computation is more important than the model.
"Wikipedia says Genetic Programming is a specialization of genetic algorithms where the objects are programs. I think this is the only thing I have ever seen where the specialization turns out to be more general than the parent."
Wikipedia says Genetic Programming is a specialization of genetic algorithms where the objects are programs. I think this is the only thing I have ever seen where the specialization turns out to be more general than the parent.
I'd also like to point out that I although all ML learns models, all those I gave can learn stuff as what amounts to a bunch of IF-THEN statements. Well GP need not be so limited. And Random Forests lose the trees in the forest (harder to interpret) but yeah, that's why I formed that particular list.
[1] http://en.wikipedia.org/wiki/Genetic_programming
[2] http://en.wikipedia.org/wiki/Genetic_algorithm