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An old article, but interesting for the same familiar reasons. I've done some work in genetic programming, most of which was done for a graduate project that I
by swernli 17y ago
An old article, but interesting for the same familiar reasons. I've done some work in genetic programming, most of which was done for a graduate project that I really enjoyed. The part of this article I resonated with the most was the end where they discuss the issue of finding problems to apply this method to. I have had some designs for evolutionary algorithms floating around, and even started parts of a framework, but keep losing motivation for it for the same reason: if I get the GP system up and running, what do I run it on? What do I have to evolve? I hate that my creativity stagnates without a well defined problem...
- marshallp 17y agoall problems where you would have applied machine learning or statistics. stock picking computer vision speech recognition discerning mathematical models of systems, effectively all of science, using symbolic regression
- mattrepl 17y agoProblems for which you have solid models and tools (statistics) to solve them are the exact sort you wouldn't use GP or any sort of evolutionary computing (EC). EC is best used when the only other option is picking random solutions from the entire solution space. At least that's what I've gathered.
- marshallp 17y agoGenetic programming is a vastly underapplied solution in modeling. You automatically get a rough mathematical model, analyze and understand and make progress with it. The scientific community hasn't really embraced what vast computational power can do. Some are catching on though - look for symbolic regression or automated scientist on google. (There's a fully automated 'robot' that does biology experiments built by a university prof in england though i'm not sure whether he usss genetic algorithms or genetic programming.)