An Hybrid Multilayer Perceptron Using Gso-ga for Software Defect Prediction
نویسنده
چکیده
Software defect prediction has turned into an expected requirement for organizations to guarantee quality and reliability of software products. The early defect prediction can encourage managers to amend and improve reliability of product. Methodologies, for example, machine learning and neural network have ended up as eminent solution for training and classification of data and can be important for defect prediction. Though, these methodologies need optimizationfor weight update, parametric improvement while performing defect prediction. In this paper a hybrid Glowworm Swarm Optimization (GSO) Genetic Algorithm (GA) to optimize the Multi-Layer Perceptron Neural Network (MLPNN) is proposed.
منابع مشابه
(SVR-GA) and multilayer perceptron optimized with GA (MLP-GA). Experimental results show that both approaches outperform conventional trading systems without prediction and a recent fuzzy trading system in terms of final equity and maximum drawdown for Hong Kong
This paper proposes an intelligent trading system using support vector regression optimized by genetic algorithms (SVR-GA) and multilayer perceptron optimized with GA (MLP-GA). Experimental results show that both approaches outperform conventional trading systems without prediction and a recent fuzzy trading system in terms of final equity and maximum drawdown for Hong Kong Hang Seng stock index.
متن کاملA Genetic Algorithm Optimized Multi- Layer Perceptron for Software Defect Prediction
Predicting the defects in software is one of the significant issues in software engineering that contributes a considerable measure toward sparing time in software generation and maintenance process. Essentially, discovering the optimal models for Software Defect Prediction (SDP) has these days transformed into one of the primary objectives of software architects. Since details and restrictions...
متن کاملPrediction of daily evaporation using hybrid support vector regression-firefly optimization algorithm and multilayer perceptron
Prediction of daily evaporation is a valuable and determinant tool in sustainable agriculture and hydrological issues, especially in the design and management of water resources systems. Therefore, in this study, the ability of artificial intelligence models of multi-layer perceptron (MLP), support vector regression (SVR), and the hybrid model of support vector regression-firefly optimization a...
متن کاملSpatiotemporal Estimation of PM2.5 Concentration Using Remotely Sensed Data, Machine Learning, and Optimization Algorithms
PM 2.5 (particles <2.5 μm in aerodynamic diameter) can be measured by ground station data in urban areas, but the number of these stations and their geographical coverage is limited. Therefore, these data are not adequate for calculating concentrations of Pm2.5 over a large urban area. This study aims to use Aerosol Optical Depth (AOD) satellite images and meteorological data from 2014 to 2017 ...
متن کاملHybrid Supervised Learning in MLP using Real-coded GA and Back-propagation
This paper addresses a classification task of pattern recognition by combining effectiveness of evolutionary and gradient descent techniques. We are proposing a hybrid supervised learning approach using real-coded GA and back-propagation to optimize the connection weights of multilayer perceptron. The following learning algorithm overcomes the problems and drawbacks of individual technique by i...
متن کامل