[aothman]

For solving local search problems, I use tabu search (hill climb with a "recently visited" list) or beam search (simultaneous hill search). Both are simple techniques that show remarkable emergent behavior. Many search problems are better phrased in terms of numerical optimization or what have you - if your problems maps a continuous space to a continuous space there's probably a standard numerical technique that solves it better than a local search hack.

For Machine Learning type applications, SVMs are very popular. Briefly, both sufficiently deep neural nets and sufficiently dimensional SVMs are arbitrarily expressive, but SVMs give you a better perspective on what is actually happening with your problem. If you're interested in Machine Learning, you should check out Andrew Moore's very well-written tutorials: http://www.autonlab.org/tutorials/list.html