Illinois Genetic Algorithms Laboratory

I am sitting on some of the EDA sessions and I can help thinking about a discussion I had with Tian-Li Yu when I was preparing the papers about the compact classifier system. The discussion was about the main differences of DSMGA when compared to eCGA or BOA. DSMGA, unlike eCGA and BOA, provides a crisp and clear presentation of the identified building blocks at the end of a run—just to mention the reason of my wondering. Once the population has converged, eCGA and BOA models just indicate that all the variables are independent. From a substructure identification point of view, the final DSM gives a clear picture of how the problem looks like. Such a property keeps getting my attention every time I sit in an EDA session.

Mental not to self: I need to talk to Tian-Li again. Genetics-based machine learning systems that can provide problem decomposition at the end of the run is one of the challenges I would like to see more solutions on.