I am an undergrad student doing very basic research on the role genetic algorithms can/do play in video games. On Youtube, there are videos of people showing how they’ve used the algorithms to teach the computer player how to play.
http://www.youtube.com/watch?v=ofVKsxeYa6U&feature=related
I understand genetic algorithms to be a search algorithm that is best used when you know the general solution you wish to achieve but not exactly. Ex. In the TSP you know you want to find shortest possible route or in an exam scheduling problem, you want all the students to be able to take their exams with the least amount of “interrupts”. In these, problems the algorithm’s uses in clear. However I’m having trouble grasping the concept of “machine learning” with g.a.
When genetic algorithms are used to teach a computer how to play, how are they “learning”?
How have they learned to play to play the game?
What is the “optimization problem” they attempt to solve?
One use is Feature Selection.
Many times, especially in text problems – but not only – your feature space is huge, and many machine learning algorithms (KNN for example) are vulnerable for non-informative features, and get worse with large amount of features.
Using Feature selection algorithm, you can reduce the dimensions of your problem – but the question is – How to chose which features are redundant?
There are many ways to do it, but one of them is using Gentic Algorithm as a search function, and try to optimize the subset of the feature you want to keep.
This use is commonly used, and is even implemented in the open source ML library Weka in the AttributeSelection package as GeneticSearch