NDLI: Genetic Algorithm Based Forecasting Model for the Employment Demand of Major in English

Content Provider	IEEE Xplore Digital Library
Author	Xia Xiaocui Liu Junshuan
Copyright Year	2013
Description	Author affiliation: Hebei Univ. of Sci. & Technol., Shijiazhuang, China (Xia Xiaocui; Liu Junshuan)
Abstract	Recently, the employment demand has been paid a lot of attention to so that the research with respect to the forecasting methods such as linear regression, artificial intelligence and the grey prediction method have been comprehensively investigated. Since the predictive focus and the characteristics of the methods are different, the outcomes of predictions cannot achieve the accuracy by simply hybridizing several methods or ignore the big prediction errors. In this paper, we propose a combined forecasting model of employment demand of student in the English major based on genetic algorithm. The combined forecasting model and the global searching ability of genetic algorithm can confirm the portfolio weights. The results show that it is feasible and effective to composite prediction model to forecast employment demand.
Starting Page	331
Ending Page	335
File Size	573506
Page Count	5
File Format	PDF
e-ISBN	9781479927920
DOI	10.1109/ISDEA.2013.479
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Industries Economics Super Alloy Employment Predictive models Cutting Parameter Milling Force Forecasting Finite Element Simulation Genetic algorithms
Content Type	Text
Resource Type	Article

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