Optimization method of maximum likelihood estimation parameter estimation based on genetic algorithms

In order to avoid computing complex derivate for maximum likelihood estimation (MLE) parameter estimation, an optimization method of MLE functions as the optimization objection was established. Genetic algorithms (GA) was utilized to obtain optimization solution, compared with conventional optimization algorithms, this method can solve MLE parameter estimation problem without estimating initialization of optimization variable. The main contribution of this paper is to propose some practical methods and improvements of using scale transform fitness, parallel operation and preserving excellence individual for dealing with the shortcoming of traditional GA. A special parameter estimation case of Weibull distribution was presented, through computer simulation, the result shows that the optimization method of MLE parameter estimation is feasible, and demonstrates that the improvement GA has high efficiency and convergence to find global optimal solution. Accordingly, it is better than traditional optimization method for parameter estimation.