Estimation of Two Parameter Birnbaum-Saunders Distribution Based on Type-II Right Censored Reliability Data Using Genetic Algorithm

The Birnbaum-Saunders (IBS) distribution is a common reliability distribution used in scientific studies. There have been studies in the literature on parameter estimates for this distribution. Furthermore, in many studies, it is recommended to use Genetic Algorithm (GA) optimization methods for parameter estimation in modeling. This article focuses on estimating the model parameters of a two-paranheter Birnbaum-Saunders distribution for Type II right censored reliability data. For parameter estimation of the Birnbaum-Saunders distribution, we propose the Genetic Algorithm (GA) method as an alternative to the Maximum Likelihood (ML) estimation method. Psi31 data is often used as an example to show the limitations of prediction methods when using inaccurate data. In addition, the performances of ML and GA methods were investigated through Monte Carlo simulation with different sample sizes and censorship rates.