GA-based learning bias selection mechanism for real-time scheduling systems

Authors:
Yeou-Ren Shiue;Ruey-Shiang Guh;Tsung-Yuan Tseng
Affiliations:
Department of Information Management, Huafan University, No. 1, Huafan Road, Shihding Township, Taipei County 22301, Taiwan, ROC;Institute of Industrial Engineering and Management, Department of Industrial Management, National Formosa University, Huwei, Yunlin, Taiwan, ROC;Department of Information Management, Huafan University, No. 1, Huafan Road, Shihding Township, Taipei County 22301, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

The use of machine learning technologies in order to develop knowledge bases (KBs) for real-time scheduling (RTS) problems has produced encouraging results in recent researches. However, few researches focus on the manner of selecting proper learning biases in the early developing stage of the RTS system to enhance the generalization ability of the resulting KBs. The selected learning bias usually assumes a set of proper system features that are known in advance. Moreover, the machine learning algorithm for developing scheduling KBs is predetermined. The purpose of this study is to develop a genetic algorithm (GA)-based learning bias selection mechanism to determine an appropriate learning bias that includes the machine learning algorithm, feature subset, and learning parameters. Three machine learning algorithms are considered: the back propagation neural network (BPNN), C4.5 decision tree (DT) learning, and support vector machines (SVMs). The proposed GA-based learning bias selection mechanism can search the best machine learning algorithm and simultaneously determine the optimal subset of features and the learning parameters used to build the RTS system KBs. In terms of the accuracy of prediction of unseen data under various performance criteria, it also offers better generalization ability as compared to the case where the learning bias selection mechanism is not used. Furthermore, the proposed approach to build RTS system KBs can improve the system performance as compared to other classifier KBs under various performance criteria over a long period.