Background Knowledge in GA-based Concept Learning
Jukka Hekanaho · 1996
We study the integration of background knowledge and concept learning genetic algorithms and show how they have beenintegrated in the system DOGMA. Our emphasis is in speeding up the inductive learning process by using suggestions from the background knowledge to direct genetic search. We don't do theory revision by patching the old theory, rather we build a new theory by using parts of the background knowledge. Results show that the methodology can lead to better results, as well as to clear savings in computational e ort, compared to learning with purely inductive GAs.