@article{Boonthiem_2017, title={A differential evolution algorithm with adaptive controlling weighted parameter for finite mixture model of some fire insurance data in Thailand}, volume={9}, url={https://ph01.tci-thaijo.org/index.php/snru_journal/article/view/93310}, abstractNote={<span class="fontstyle0">We propose an adaptive differential evolution algorithm to estimate the parameter of finite mixture<br />model. The finite mixture model separates two parts of parameter set. The first part consists of a parameter set<br />of distributions that used for our experiment. The second part consists of a weighted parameter set of each<br />component that are positive real value and the combination of them equal to 1. We perform an experiment to<br />obtain an adaptive differential evolution algorithm for the finite mixture model by controlling the parameter set<br />of the second part using a technique of weighting with constant such that parameter set of the second part<br />satisfies condition of the finite mixture model. In this paper, we use an adaptive differential evolution algorithm<br />to estimate the parameter of combination of nine distributions: Rayleigh distribution, logistic distribution, gamma<br />distribution, Pareto distribution, log-logistic distribution, normal distribution, Weibull distribution, log-normal<br />distribution, and exponential distribution for 47 claimed size data of fire insurance of an insurance company in<br />Thailand which use K-S test statistic for the objective function. The results show that the best K-S test statistic<br />value of finite mixture model which equal to 0.0535 less than the best single model which equal 0.0811.<br />Therefore, the parameter set of the finite mixture model is 34.03 percent better.</span>}, number={2}, journal={Creative Science}, author={Boonthiem, Somchit}, year={2017}, month={Jun.}, pages={491–501} }