“…5, in EDA-IGA, new populations are generated jointly by IGA and EDA. And IGA and EDA share an original population to generate two different sub-populations according to their respective algorithm mechanisms, and then mix these two sub-populations to complete a generation of evolution (Dang et al, 2022).…”
Section: Estimation Of Distribution Algorithmsmentioning
In order to accurately predict the possible gas emission quantity in coal mines, it is proposed to use the multi-thread calculation of the Immune Genetic Algorithm (IGA) and injection of vaccines to improve the accuracy of prediction and combine the Estimation of Distribution Algorithm (EDA) to the distribution probability of excellent populations. Calculating, and selecting excellent populations for iteration, optimize the population generation process of the Immune Genetic Algorithm, so that the population quality is continuously optimized and improved, and the optimal solution is obtained, thereby establishing a gas emission quantity prediction model based on the Immune Genetic Algorithm and Estimation of Distribution Algorithm. Using the 9136 mining face with gas emission hazards in a coal mine from Shandong Province in China as the prediction object, the absolute gas emission quantity is used to scale the gas emission quantity, and it is found that the model can accurately predict the gas emission quantity, which is consistent with the on-site emission unanimous. In the prediction comparison with IGA, it is found that the accuracy of the prediction results has increased by 9.51%, and the number of iterations to achieve the required goal has been reduced by 67%, indicating that the EDA has a better role in optimizing the population update process such as genetic selection of the IGA. Comparing the prediction results of other models, it is found that the prediction accuracy of the EDA-IGA is 94.93%, which is the highest prediction accuracy, indicating that this prediction model can be used as a new method for the prediction of coal mine gas emission.
“…5, in EDA-IGA, new populations are generated jointly by IGA and EDA. And IGA and EDA share an original population to generate two different sub-populations according to their respective algorithm mechanisms, and then mix these two sub-populations to complete a generation of evolution (Dang et al, 2022).…”
Section: Estimation Of Distribution Algorithmsmentioning
In order to accurately predict the possible gas emission quantity in coal mines, it is proposed to use the multi-thread calculation of the Immune Genetic Algorithm (IGA) and injection of vaccines to improve the accuracy of prediction and combine the Estimation of Distribution Algorithm (EDA) to the distribution probability of excellent populations. Calculating, and selecting excellent populations for iteration, optimize the population generation process of the Immune Genetic Algorithm, so that the population quality is continuously optimized and improved, and the optimal solution is obtained, thereby establishing a gas emission quantity prediction model based on the Immune Genetic Algorithm and Estimation of Distribution Algorithm. Using the 9136 mining face with gas emission hazards in a coal mine from Shandong Province in China as the prediction object, the absolute gas emission quantity is used to scale the gas emission quantity, and it is found that the model can accurately predict the gas emission quantity, which is consistent with the on-site emission unanimous. In the prediction comparison with IGA, it is found that the accuracy of the prediction results has increased by 9.51%, and the number of iterations to achieve the required goal has been reduced by 67%, indicating that the EDA has a better role in optimizing the population update process such as genetic selection of the IGA. Comparing the prediction results of other models, it is found that the prediction accuracy of the EDA-IGA is 94.93%, which is the highest prediction accuracy, indicating that this prediction model can be used as a new method for the prediction of coal mine gas emission.
“…In probabilistic logic sampling we use ancestral node ordering, i.e., we sample a node X i after sampling from all its parent nodes Pa(X i ) which results in a fixed value pa(x i ) (forward sampling scheme). Efficient sampling schemes to promote the visit of promising regions and avoid premature convergence have been recently proposed for Gaussian Bayesian networks [80].…”
The automatic induction of machine learning models capable of addressing supervised learning, feature selection, clustering and reinforcement learning problems requires sophisticated intelligent search procedures. These searches are usually performed in the possible model structure spaces, leading to combinatorial optimization problems, and in the parameter spaces, where it is necessary to solve continuous optimization problems. This paper reviews how the estimation of distribution algorithms, a kind of evolutionary algorithm, can be used to address these problems. Topics include preprocessing, mining association rules, selecting variables, searching for the optimal supervised learning model (both probabilistic and nonprobabilistic models), finding the best hierarchical, partitional or probabilistic clustering, obtaining the optimal policy in reinforcement learning and performing inference and structural learning in Bayesian networks for association discovery. Interesting guidelines for future work in this area are also provided.
“…5 , in EDA-IGA, new populations are generated jointly by IGA and EDA. And IGA and EDA share an original population to generate two different sub-populations according to their respective algorithm mechanisms, and then mix these two sub-populations to complete a generation of evolution [ 25 ]. Fig.…”
Section: Estimation Of Distribution Algorithmsmentioning
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