標題: Nonlinear Deterministic Frontier Model Using Genetic Programming
作者: Chen, Chin-Yi
Huang, Jih-Jeng
Tzeng, Gwo-Hshiung
科技管理研究所
Institute of Management of Technology
關鍵字: Technical efficiency;symbolic regression;genetic programming (GP);Monte Carlo simulation;data envelopment analysis (DEA)
公開日期: 2009
摘要: In economics, several parametric regression-based models have been proposed to measure the technical efficiency of decision making units (DMUs). However, the problem of misspecification restricts the use of these methods. In this paper, symbolic regression is employed to obtain the approximate optimal production function automatically using genetic programming (GP). Monte Carlo simulation is used to compare the performance of data envelopment analysis (DEA), deterministic frontier analysis (DFA) and GP-based DFA with respect to three different production functions and sample sizes. The simulated results indicated that the proposed method has better performance than that of others with respect to nonlinear production functions.
URI: http://hdl.handle.net/11536/13178
http://dx.doi.org/10.1007/978-3-642-02298-2_111
ISBN: 978-3-642-02297-5
ISSN: 1865-0929
DOI: 10.1007/978-3-642-02298-2_111
期刊: CUTTING-EDGE RESEARCH TOPICS ON MULTIPLE CRITERIA DECISION MAKING, PROCEEDINGS
Volume: 35
起始頁: 753
結束頁: 760
顯示於類別:會議論文


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