Software reliability growth models (SRGMs) are very important for estimating and predicting software reliability. However, because the assumptions of traditional parametric SRGMs (PSRMs) are usually not consistent with the real conditions, the prediction accuracy of PSRMs are hence not very satisfying in most cases. In contrast to PSRMs, the non-parametric SRGMs (NPSRMs) which use machine learn...

For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation estimate compressive strength fc? GPC made employing FA. To build a model, consistent, extensive and reliable data base is compiled...

This paper presents a two-stage genetic mechanism for the migration-based load balance of virtual machine hosts (VMHs) in cloud computing. Previous methods usually assume this issue as job-assignment optimization problem and only consider current VMHs' loads; however, without considering loads VMHs after balancing, these can gain limited effectiveness real environments. In study, two genetic-ba...

Vegetable-oil-based biolubricants are an excellent alternative to conventional lubricants. Instead of focusing on novel feedstocks, these should be further elucidated based their fatty acid composition, which influences tribological properties. Therefore, the study utilises gene expression programming (GEP) derive a boundary lubricity model for vegetable-oil-derived trimethylolpropane (TMP) est...

This thesis work aims at improving the problem solving ability of the Gene Expression Programming (GEP) algorithm to fulfill complex data mining tasks by preserving and utilizing the self-emergence of structures during its evolutionary process. The main contributions include the investigation of the constant creation techniques for promoting good functional structures emergent in the evolution,...

The river cross-section elevation data are an essential parameter for engineering. However, due to the difficulty of mountainous surveys, existing bathymetry investigation techniques cannot be easily applied in a narrow and shallow field. Therefore, this study aimed establish model suitable areas utilizing unmanned aerial vehicle (UAV) equipped with multispectral camera machine learning-based g...

The use of soft computing techniques is becoming more common in providing solutions to complex engineering problems such as the concrete breakout strength anchor. Available include semi-empirical equations that are known over or underpredict and some incapable generating predictive equations. This study proposes a gene expression programming (GEP)-based mathematical model predict edge capacity ...

Abstract This study applies three methods, Gene Expression Programming (GEP), M5 tree (M5T) model and optimized Artificial Neural Network by Genetic Algorithm (ANN-GA) for estimation of reference evapotranspiration in Ahvaz Dezful the southwest Iran. Comparison between results FAO Penman - Monteith (FPM) method mentioned methods shows that ANN-GA with Levenberg-Marquardt training is best M5T se...

The application of waste materials in concrete is gaining more popularity for sustainable development. adaptation this approach not only reduces the environmental risks but also fulfills requirement material. This study used novel algorithms machine learning (ML) to forecast splitting tensile strength (STS) containing recycled aggregate (RA). gene expression programming (GEP), artificial neural...