One of the challenges in reservoir management is determining the fracture density (FVDC) in reservoir rock. Given the high cost of coring operations and image logs, the ability to predict FVDC from various petrophysical input variables using a supervised learning basis calibrated to the standard well is extremely useful. In this study, a novel machine learning approach is developed to predict FVDC from 12-input variable well-log based on feature selection. To predict the FVDC, combination of two networks of multiple extreme learning machines (MELM) and multi-layer perceptron (MLP) hybrid algorithm with a combination of genetic algorithm (GA) and particle swarm optimizer (PSO) has been used. We use a novel MELM-PSO/GA combination that has never been used before, and the best comparison result between MELM-PSO-related models with performance test data is RMSE = 0.0047 1/m; R2 = 0.9931. According to the performance accuracy analysis, the models are MLP-PSO < MLP-GA < MELM-GA < MELM-PSO. This method can be used in other fields, but it must be recalibrated with at least one well. Furthermore, the developed method provides insights for the use of machine learning to reduce errors and avoid data overfitting in order to create the best possible prediction performance for FVDC prediction.
Now-a-days software has a great impact on different aspects of human life. Software systems are responsible for safety of major critical tasks. To prevent catastrophic malfunctions, promising quality testing techniques should be used during software development. Software testing is an effective technique to catch defects, but it significantly increases the development cost. Therefore, automated testing is a major issue in software engineering. Search-Based Software Testing (SBST), specifically genetic algorithm, is the most popular technique in automated testing for achieving appropriate degree of software quality. In this paper TLBO, a swarm intelligence technique, is proposed for automatic test data generation as well as for evaluation of test results. The algorithm is implemented in EvoSuite, which is a reference tool for search-based software testing. Empirical studies have been carried out on the SF110 dataset which contains 110 java projects from the online code repository SourceForge and the results show that the TLBO provides competitive results in comparison with major genetic based methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.