| Genetic Programming and the wider field of Evolutionary Computation and Genetic Algorithms are really good at some kinds of optimization. If the problem fits into a tree form, then Genetic Programming programming is your best friend. If the problem can be encoded onto a set of substrings of 0s and 1s, then Genetic Algorithms are your best friend. Likewise if your problem can be encoded using floating point numbers, then Evolutionary Strategies is your best friend. Now... I could ruffle some feathers by saying that Genetic Algorithms and Evolutionary Strategies aren't really all that different. Same algorithms could be used for both. But historicaly they they came roughly at similar times frome different places on earth, Illinois vs Germany. Back to GP. The cool thing about GP is that when the solution is evolved you have THE solution to the problem. No futzing about with how or what the results mean. A big problem in the past is that GP doesn't scale well like neural networks. It is not embarrassingly parallel. It has been very limited by the types of hardware/architectures available. But GP is a great field of exploration! |