Predicting the shift cycle of the net-cage by the Bayesian network based on immune evolutionary algorithms

Abstract:

By taking the monitoring data of Xiangshan Bay from the year of 2000 to 2006 as the training data and referring to the prior knowledge, a Bayesian network was constructed through the incremental learning based on the immune heredity algorithm. The model can effectively express the causal relationship among the various indicators in the net-cage aquaculture environment, and the shift cycle of the net-cage aquaculture at Xiangshan can be predicted. The result showed that the appraisal accuracy reached 91.7%, which meant that this method is feasible.