An efficiency‐improved genetic algorithm and its application on multimodal functions and a 2D common reflection surface stacking problem

Acuna, Y.P. Villa; Sun, Yimin

doi:10.1111/1365-2478.12920

Cited by 17 publications

(6 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several performances of the method were evaluated by experiments. The comparison results demonstrated that this algorithm has excllent performance and its prediction accuracy reaches 98.6% [17].…”

Section: Related Workmentioning

confidence: 95%

Mobile User English Learning Pattern Recognition Model based on Integrated Learning

Zhang,

Ren

2024

SCPE

View full text Add to dashboard Cite

The research in the field of mobile assisted language learning has a long history. It basically follows the route from theory to application practice, but there are few process studies on learners’ individual language skills learning behavior based on mobile platform data. This research takes vocabulary learning as the starting point, and constructs a mobile user English learning pattern recognition model with improved Stacking integration algorithm. The purpose of this study is to identify different learning modes by analyzing the learning behavior and learning data of mobile users, and to provide personalized learning suggestions for users. The evaluation goal of this study is the accuracy and robustness of the mobile users’ English learning pattern recognition model, and the accuracy is the classification accuracy of the model for different learning patterns. Robustness is the stability and consistency of the model in different situations. In order to evaluate the integrated English learning mode of mobile users, the study first divides the collected learning data set into training set and test set. In this step, the method used in the study is cross-validation, which aims to reduce the difference of evaluation results caused by different data sets. For the relevant features in mobile users’ learning data, the features extracted by the research include learning behavior and learning progress, which can accurately reflect the learning mode. Then, the integrated learning method is used to train the model, and the best parameter combination is selected through the training set. Finally, the study uses the test set to evaluate the trained model, and calculates the accuracy and recall index of the model on the test set. Through this evaluation method, the evaluation results of the integrated learning pattern recognition model in terms of accuracy and robustness are obtained, and reference is provided for the improvement of the model. The model proposed in this study is suitable for a large number of user data. Because the learning behavior of users is influenced by personal habits, there are limitations in obtaining enough high-quality data, so the labeling of data is subjective. It is still a challenge to select the most representative features of user learning behavior extracted from the model. The feature selection method can lead to different results, and the process requires a lot of human intervention. The experiment conducted mining and analysis on user learning behavior data of a domestic English vocabulary learning APP. Compared with the confusion matrix of the traditional Stacking model, the improved Stacking model has a stronger ability to distinguish user learning patterns. According to the formula, the accuracy of the improved Stacking model is 91.29%; The accuracy of traditional Stacking model is 90.71%. The ROC curve of the improved Stacking model is smoother than the three single models. Its AUC value is 0.85, which is the same as that of XGBoost. The function is also improved compared with the traditional Stacking model, Logistic Regression (LR) and Random Forest (RF) model. Therefore, the Stacking integrated model owns the best forecast performance and can accurately predict the long-term learning mode of users. In this study, the English learning patterns of mobile users are identified by the method of integrated learning, so that the prediction results of multiple basic learners can be integrated, and the complementarity between different learners can be effectively dealt with, thus improving the generalization ability of the model. This model aims at identifying the English learning patterns of mobile users, and can accurately identify the learning patterns of users by analyzing their learning behaviors on mobile devices. This is of great significance for personalized English learning recommendation. The English learning pattern recognition model for mobile users proposed in this study can identify users’ learning patterns by analyzing their learning behaviors, thus providing personalized learning support and suggestions. In the process of digital manufacturing, they can learn from the idea of learning pattern recognition model and identify the patterns and laws in the production process by analyzing production data and process parameters, so as to optimize the production process and improve production efficiency.

show abstract

Section: Related Workmentioning

confidence: 95%

Mobile User English Learning Pattern Recognition Model based on Integrated Learning

Zhang,

Ren

2024

SCPE

View full text Add to dashboard Cite

show abstract

“…It can improve the extensive search ability of the algorithm in the solution space and reduce the dependence on the initial solution, thus effectively avoiding the problem of falling into the local optimal solution and being unable to find the global optimal solution. a) Design of chromosome coding: The dimension of the chromosome gene vector in real coding is determined by the number of weights and thresholds present in the backpropagation neural network [22], and the formula is as follows:…”

Section: ) Optimization Of Back-propagation Neural Network Algorithmmentioning

confidence: 99%

Research on Evaluation Method of Urban Human Settlement Environment Quality Based on Back Propagation Neural Network

Zhang,

Song

2023

IJACSA

View full text Add to dashboard Cite

In order to improve people's living experience, a method for evaluating the quality of urban human settlements based on back propagation neural network is proposed. Firstly, the initial evaluation index system is constructed, the initial evaluation index system is screened, and the final evaluation index system is constructed by using the remaining evaluation indexes. Then, the back propagation neural network is constructed to build an evaluation model, and the evaluation model is trained through the processes of network initialization, hidden layer output calculation and output layer output calculation. Finally, the improved genetic algorithm is used to optimize the back propagation neural network, improve the evaluation performance of the back propagation neural network, and realize the evaluation of human settlements quality. The experimental results show that the accuracy of the evaluation results of urban human settlements quality output by the trained back-propagation neural network model reaches 96.3%, which has a good effect.

show abstract

“…There are at least two types for each genetic operator, and their combination with each other can give various results. An attempt to pick better parameters was made in the [11], where the authors created their own program, but the result can differ depending on the assigned task. The present study aims to determine the optimal parameters of the genetic algorithm for doctors in the task of an individualized treatment strategy search.…”

Section: Analysis Of the Latest Research And Publicationsmentioning

confidence: 99%

“…As a result, a new individual is obtained, which contains information about its ancestors. There are many types of individual crossover [11].…”

Section: Fig 1 Flowchart Of Genetic Algorithmmentioning

confidence: 99%

Setting Up the Genetic Algorithm for the Individualized Treatment Strategy Searching

Dydyk,

Nosovets,

Babenko

2020

HAIT

View full text Add to dashboard Cite

The genetic algorithm is a verified mechanism for optimization task solutions. Being a heuristic algorithm, it allows speeding up the task solving by the biological evolution principle use. Recently, this algorithm was offered as a method of an individualized treatment strategy search, where it was necessary to optimize the patient’sstatein the distant period, going through various treatment combinations.In that research as an optimization function, the additive convolution function of the patient’sstateindicatorsin the distant period was used, obtained with the help of Saaty analytic hierarchy process, which is one of the multi-criteria decision making methods. Despite showing good results, the genetic algorithm was set with standard parameters. Taking into consideration a big quantity of the parameters, the present study has the aim to find the optimum parameters for the algorithm. First of all, it is necessary for those, who incorporate this algorithm in their work, namely doctors, when they need to prescribe a treatment for a patient. The study describes the analysis of various genetic algorithm parameters and their use in algorithm test launches for individualized treatment strategy search. Also, the optimal patient’s input parameter subsets wereselected,using the correlation feature selection criterion. The selected parameters were necessary for modeling indicators of the patient’sstateafter treatment.Modeling wasperformed via random forest classifier with preliminarydivided total sample intotraining(eighty percent) and testing(twenty percent) ones. Two different databases of patients with congenital heart diseases were usedfor the study, allowing the optimal parameters being more reliable for their future use. In the end, it all allows finding the parameters, which are first of allexclusively recommendatoryto the doctors before using the algorithm.

show abstract

An efficiency‐improved genetic algorithm and its application on multimodal functions and a 2D common reflection surface stacking problem

Cited by 17 publications

References 46 publications

Mobile User English Learning Pattern Recognition Model based on Integrated Learning

Mobile User English Learning Pattern Recognition Model based on Integrated Learning

Research on Evaluation Method of Urban Human Settlement Environment Quality Based on Back Propagation Neural Network

Setting Up the Genetic Algorithm for the Individualized Treatment Strategy Searching

Contact Info

Product

Resources

About