Using Dynamic Adjusting NGHS-ANN for Predicting the Recidivism Rate of Commuted Prisoners

Po-Chou Shih, Chui-Yu Chiu and Chi-Hsun Chou
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Po-Chou Shih: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
Chui-Yu Chiu: Industrial Engineering and Management, National Taipei University of Technology, Taipei 10632, Taiwan
Chi-Hsun Chou: Taoyuan Prison, Agency of Corrections, Ministry of Justice, Taoyuan 33056, Taiwan

Mathematics, 2019, vol. 7, issue 12, 1-25

Abstract: Commutation is a judicial policy that is implemented in most countries. The recidivism rate of commuted prisoners directly affects people’s perceptions and trust of commutation. Hence, if the recidivism rate of a commuted prisoner could be accurately predicted before the person returns to society, the number of reoffences could be reduced; thereby, enhancing trust in the process. Therefore, it is of considerable importance that the recidivism rates of commuted prisoners are accurately predicted. The dynamic adjusting novel global harmony search (DANGHS) algorithm, as proposed in 2018, is an improved algorithm that combines dynamic parameter adjustment strategies and the novel global harmony search (NGHS). The DANGHS algorithm improves the searching ability of the NGHS algorithm by using dynamic adjustment strategies for genetic mutation probability. In this paper, we combined the DANGHS algorithm and an artificial neural network (ANN) into a DANGHS-ANN forecasting system to predict the recidivism rate of commuted prisoners. To verify the prediction performance of the DANGHS-ANN algorithm, we compared the experimental results with five other forecasting systems. The results showed that the proposed DANGHS-ANN algorithm gave more accurate predictions. In addition, the use of the threshold linear posterior decreasing strategy with the DANGHS-ANN forecasting system resulted in more accurate predictions of recidivism. Finally, the metaheuristic algorithm performs better searches with the dynamic parameter adjustment strategy than without it.

Keywords: artificial neural networks; metaheuristic optimization; forecast; recidivism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
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