Dynamic Parameter Adjustment in CSMA/ECA

2010 ◽  
pp. 13-24 ◽  
Author(s):  
Jaume Barcelo ◽  
Boris Bellalta ◽  
Cristina Cano ◽  
Anna Sfairopoulou ◽  
Miquel Oliver
Keyword(s):  
Dynamic Parameter ◽  
Parameter Adjustment ◽  
Dynamic Parameter Adjustment

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

Mathematics ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 1187 ◽  
Author(s):  
Po-Chou Shih ◽  
Chui-Yu Chiu ◽  
Chi-Hsun Chou
Keyword(s):  
Harmony Search ◽  
Dynamic Parameter ◽  
Genetic Mutation ◽  
Recidivism Rate ◽  
Dynamic Adjustment ◽  
Parameter Adjustment ◽  
Forecasting System ◽  
Adjustment Strategies ◽  
Searching Ability ◽  
Dynamic Parameter Adjustment

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.

Differential evolution with individual-dependent and dynamic parameter adjustment

2017 ◽  
Vol 22 (17) ◽  
pp. 5747-5773 ◽  
Author(s):  
Gaoji Sun ◽  
Jin Peng ◽  
Ruiqing Zhao
Keyword(s):  
Differential Evolution ◽  
Dynamic Parameter ◽  
Parameter Adjustment ◽  
Dynamic Parameter Adjustment
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