WCCI (CEC) 2006 Invited Talk:
Coevolutionary Learning by Melanie Mitchell
This invited talk is about making better learning systems. The idea is evolving both “classifier” and “training set” together (co-evolving them in a competitive setting). The way that this works well is by spatial co-evolution. This “spatial” property is very important and other cases (non-spatial co-evolution or spatial evolution) did not work well. However, I should note that none of the experiments was about real classifier. One of them is designing a cellular automata to do some specific task (majority voting) and the other is fitting a tree-like structure (actually a GP tree) to some function.
She thoughts that spatial Co-evolution improves performance because:
-maintain diversity in population
-produces “arm races” with ICs targeting weaknesses in CA and CAs adapting to overcome weaknesses.
Also you may like to read a quotation. This is one for you: “Co-evolution is all about information propagation!”
Finally I asked her if it is reasonable to evolve training set for a classification task? By evolving them too, the pdf of the training set will changed, and this means that the evolved new samples may be completely different from the pdf of the test set, i.e. the classifier evolved in a way to optimize a loss function for a distribution other than “population” (or test or true) distribution.
She answered me, I did not satisfied completely by her answer.