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Future Internet 2018, 10(8), 71; https://doi.org/10.3390/fi10080071

Hybrid Approach with Improved Genetic Algorithm and Simulated Annealing for Thesis Sampling

1
XianDa College of Economics and Humanities, Shanghai International Studies University, East Tiyuhui Road 390, Shanghai 200083, China
2
School of Computer Engineering and Science, Shanghai University, Shangda Road 99, Shanghai 200444, China
3
Information Centre, Shanghai Municipal Education Commission, Dagu Road 100, Shanghai 200003, China
4
Faculty of Foreign Languages, Ningbo University, Fenghua Road 818, Ningbo 315211, China
5
Department of Education Evaluation Research, Shanghai Education Evaluation Institute, South Shaanxi Road 202, Shanghai 200031, China
*
Author to whom correspondence should be addressed.
Received: 11 July 2018 / Revised: 27 July 2018 / Accepted: 27 July 2018 / Published: 30 July 2018
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Abstract

Sampling inspection uses the sample characteristics to estimate that of the population, and it is an important method to describe the population, which has the features of low cost, strong applicability and high scientificity. This paper aims at the sampling inspection of the master’s degree thesis to ensure their quality, which is commonly estimated by random sampling. Since there are disadvantages in random sampling, a hybrid algorithm combined with an improved genetic algorithm and a simulated annealing algorithm is proposed in this paper. Furthermore, a novel mutation strategy is introduced according to the specialty of Shanghai’s thesis sampling to improve the efficiency of sampling inspection; the acceleration of convergence of the algorithm can also take advantage of this. The new algorithm features the traditional genetic algorithm, and it can obtain the global optimum in the optimization process and provide the fairest sampling plan under the constraint of multiple sampling indexes. The experimental results on the master’s thesis dataset of Shanghai show that the proposed algorithm well meets the requirements of the sampling inspection in Shanghai with a lower time-complexity. View Full-Text
Keywords: sampling; genetic algorithm; simulated annealing algorithm; thesis and dissertation sampling; mutation strategy sampling; genetic algorithm; simulated annealing algorithm; thesis and dissertation sampling; mutation strategy
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Johnson, S.; Han, J.; Liu, Y.; Chen, L.; Wu, X. Hybrid Approach with Improved Genetic Algorithm and Simulated Annealing for Thesis Sampling. Future Internet 2018, 10, 71.

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