Parameter Estimations of Normal Distribution via Genetic Algorithm and Its Application to Carbonation Depth

Abstract

In this paper, we propose a method for estimating Normal distribution parameters using genetic algorithm. The main purpose of this research is to identify the most efficient estimators among three estimators for Normal distribution; Maximum likelihood method (ML), the least square method (LS), and genetic algorithm (GA) via numerical simulation and three real data, carbonation depth of Concrete Girder Bridges data examples which are based on performance measures such as The Root Mean Square Error (RMSE), Kolmogorov-Smirnov test, and Chi squared test. The simulation studies are conducted to evaluate the performances of the proposed estimators and provide statistical analysis of the real data set. The numerical results, x^2, show that the genetic algorithm performs better than other methods for actual data and simulated data unless the sample size is small.