Efficient genetic programmming for finding good generalizing Boolean functions

This paper shows how genetic programming (GP) can help in finding generalizing Boolean functions when only a small part of the function values are given. The selection pressure favours functions having a few subfunctions as possible while only using essential variables, so the resulting functions should have good generalization properties. For efficiency no S-expressions are used for representation, but a special case of directed acyclic graphs known as ordered binary decision diagrams (OBDDs), making it possible to learn the 20-multiplexer. (orig.)SIGLEAvailable from TIB Hannover: RR 8071(97-4)+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman