I'm looking into doing research with Prof. Xi this next semester under the heading of genetic algorithms (related to evolutionary computation, evolutionary strategies, genetic programming, evolutionary programming and so on). It's being used in everything from art to artificial intelligence. Xi appears to be interested by neural networks but says that the field is "wide open" to me. He handed me a book called An Introduction to Genetic Algorithms by Melanie Mitchell and told me to copy the first and last chapter of it.
"If you read the first 30 pages, you will understand 70% of the field," he told me.
Do I really need anymore motivation than that? The application of GA (or EC or ES or EP or GP) in the world of physics is not a very well-studied issue or, at least, that is what my seach for information has led me to believe so far. I'm really hoping to find some interesting uses of GAs to solve physical problems and not just engineering issues, like most efficient wing shape (though that is an interesting application).
Anyone seen anything? Oh, I should note that there are lots of libraries for working with genetic algorithms.
Scientific Python SciPy seems especially nice, if only because I really enjoy writing Python code. Unfortunately, it depends on ATLAS and since I'm a developer at Lunar Linux, I'm trying to build ATLAS from source so that I can write up a BUILD to add to our moonbase. As of yet, ATLAS is kicking my ass.
I think I might just cheat when I come home from my study time and install one of the binaries at the SciPy site. Ah well. The things I must sacrifice for the sake of science.
Update: Scientific Python and SciPy are not the same. Yeesh.