Chiu, ChaochangChaochangChiuJIH-TAY HSULin, Chih YungChih YungLin2023-04-272023-04-272001-01-01354043074103029743https://www.scopus.com/inward/record.uri?eid=2-s2.0-84957831641&doi=10.1007%2f3-540-45554-X_75&partnerID=40&md5=856b3ffc3fdd572f3def22c291a5bb2ehttps://scholars.lib.ntu.edu.tw/handle/123456789/630673Milk yield forecasting can help dairy farmers to deal with the continuously changing condition all year round and to reduce the unnecessary overheads. Several variables (somatic cell count, pariety, day in milk, milk protein content, milk fat content, season) related to milk yield are collected as the parameters of the forecasting model. The use of an improved Genetic Programming (GP) technique with dynamic learning operators is proposed and achieved with acceptable prediction results.Dynamic mutation; Genetic programming; Milk yield predictionconference paper10.1007/3-540-45554-X_752-s2.0-84957831641https://api.elsevier.com/content/abstract/scopus_id/84957831641