Parameter Estimation for a Model of Ionizing Radiation Effects on Targeted Cells using Genetic Algorithm and Pattern Search Method
A mechanistic model has been used to explain the effect of radiation. The model consists of parameters which represent the biological process following ionizing radiation. The parameters in the model are estimated using local and global optimization algorithms. The aim of this study is to compare the efficiency between local and global optimization method, which is Pattern Search and Genetic Algorithm respectively. Experimental data from the cell survival of irradiated HeLa cell line is used to find the minimum value of the sum of squared error (SSE) between experimental data and simulation data from the model. The performance of both methods are compared based on the computational time and the value of the objective function, SSE. The optimization process is carried out by using the built-in function in MATLAB software. The parameter estimation results show that genetic algorithm is more superior than pattern search for this problem.