Background Knowledge in GA-based Concept Learning
نویسنده
چکیده
We study the integration of background knowledge and concept learning genetic algorithms and show how they have been integrated 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
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