TY - JOUR
T1 - Improving Learning of Genetic Rule-Based Classifier Systems
AU - McAulay, Alastair D.
AU - Oh, Jae Chan
PY - 1994
Y1 - 1994
N2 - A genetic classifier system is reviewed and used for learning rules for classification. Two new strategies are described that enable all the letters of the alphabet to be learned. A “remembering” strategy locks in good rules to overcome forgetting that otherwise occurs during learning. A “specializing” strategy fine tunes the search process for rules. Experiments and an encoding scheme are described. Results show, for the first time, that a genetic classifier-type system can learn to classify all the letters of the alphabet. Further, computer experiments show that the new strategies result in faster and more robust classification involving images of varying position, size, and shape.
AB - A genetic classifier system is reviewed and used for learning rules for classification. Two new strategies are described that enable all the letters of the alphabet to be learned. A “remembering” strategy locks in good rules to overcome forgetting that otherwise occurs during learning. A “specializing” strategy fine tunes the search process for rules. Experiments and an encoding scheme are described. Results show, for the first time, that a genetic classifier-type system can learn to classify all the letters of the alphabet. Further, computer experiments show that the new strategies result in faster and more robust classification involving images of varying position, size, and shape.
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U2 - 10.1109/21.259696
DO - 10.1109/21.259696
M3 - Article
AN - SCOPUS:0028256815
SN - 0018-9472
VL - 24
SP - 152
EP - 159
JO - IEEE Transactions on Systems, Man and Cybernetics
JF - IEEE Transactions on Systems, Man and Cybernetics
IS - 1
ER -