Research On Intelligent Test Paper Generating Strategy Based On Genetic Algorithm And BP Neural Network

DOI

10.2991/jimec-17.2017.35How to use a DOI?

Keywords

genetic algorithm; BP neural network; intelligent test paper generation

Abstract

The prevalence of MOOC (Massive Open Online Course), while providing us with a new efficient way of learning, demands an evolved online test system for certificating attended students properly. As examination of each course still being the simple and powerful way to check out, generating test paper as the core of exam system, is particularly important. Therefore, we present an enhanced method based on BP neural network optimizing the weight coefficient of objective function in paper generating model. After the Restructuring of mutation operator, and adjusting of crossover operator with the adaptive function, the performance of genetic algorithm modified for efficient test paper generating has been improved further. Experimental results show that the improved algorithm presented in this paper is efficient for the problem of genetic algorithm in premature and local optimization.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Chao Liu
AU  - Yan Hu
PY  - 2017/10
DA  - 2017/10
TI  - Research On Intelligent Test Paper Generating Strategy Based On Genetic Algorithm And BP Neural Network
BT  - Proceedings of the 2017 2nd Joint International Information Technology, Mechanical and Electronic Engineering Conference (JIMEC 2017)
PB  - Atlantis Press
SP  - 157
EP  - 160
SN  - 2352-538X
UR  - https://doi.org/10.2991/jimec-17.2017.35
DO  - 10.2991/jimec-17.2017.35
ID  - Liu2017/10
ER  -