GAs a Solution Solution

No, not a typo. GAs have typically been set to work to generate a solution to a fixed problem.

My idea is to create a solution that generates solutions eg.

Using the MNIST dataset of hand written digits, you could breed a network that recognises digits by breeding networks until the connections and weights define a network that can solve the problem and you could optimise it for the least number of connections and nodes. This, however, is not a general solution to building recognition networks. This is very specific to the set of data shown. What would be more useful and interesting would be to define a set of rules that can be applied to build a network based on the data set in real time. A GA solution that can create a network that adapts to the data supplied. Maybe the network could be applied to shape recognition or general hand writing to text conversion.

One of my early areas of interest, was using a GA to create a compression algorithm. I succeeded in creating compressed versions of image files, but each compression was specific to the file and took many generations to breed. A better use of GAs, and similar to that described above, would be to have the GA find a generic compression solution. This would require a data set that consisted of multiple files of different types to compress, and two parts to the process, a compression cycle where the individual can see the source file and develop a compressed data set, and a decompression cycle where the source file is not available and must be reconstructed.