Evolutionary support vector machine inference system for construction management

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• 作者：Min-Yuan Cheng ; Yu-Wei Wu
• 关键词：Fast messy genetic algorithms ; Support vector machine ; Object-oriented system development
• 刊名：Automation in Construction
• 出版年：2009
• 出版时间：August 2009
• 年：2009
• 卷：18
• 期：5
• 页码：597-604
• 全文大小：532 K

文摘

Problems in construction management are complex, full of uncertainty, and vary based on site environment. Two tools, the fast messy genetic algorithms (fmGA) and support vector machine (SVM), have been successfully applied to solve various problems in construction management. Considering the characteristics and merits of each, this paper combines the two to propose an Evolutionary Support Vector Machine Inference Model (ESIM). In the ESIM, the SVM is primarily employed to address learning and curve fitting, while fmGA addresses optimization. This model was developed to achieve the fittest C and γ parameters with minimal prediction error. This research further integrates the developed ESIM with an object-oriented (OO) computer technique to create an Evolutionary Support Vector Machine Inference System (ESIS). Simulations conducted to demonstrate the robustness of the model in application indicate that ESIS may be used as a multifarious intelligent decision support system in decision-making to help solve a wide range of construction management problems.