Goyal, G.Sharma, K.AnshumanMittal, V.Singla, B.Das, M.Mohan, B.R.2026-02-0620242024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024, 2024, Vol., , p. -https://doi.org/10.1109/IATMSI60426.2024.10503224https://idr.nitk.ac.in/handle/123456789/29073Software reliability is a paramount determinant of software quality. In this research paper, we delve into utilizing Genetic Algorithms (GAs) for feature selection and classification. We undertake a comprehensive evaluation and comparative analysis of Machine Learning models, specifically Random Forest and Logistic Regression, both with and without Genetic Algorithmdriven feature selection. Our findings substantiate the significant impact of Genetic Algorithms in improving the accuracy of software reliability analysis. © 2024 IEEE.Bio-OptimizationChi-Squared StatisticGenetic AlgorithmsLogistic RegressionMachine LearningModel EvaluationPrincipal Component AnalysisRandom ForestSoftware Reliability AnalysisHybrid Genetic Algorithm and Machine Learning Approach for Software Reliability Assessment in Safety-Critical Systems