Most of the existing parameter estimation methods require much auxiliary information and is not easy to handle for practical applications. In this paper , we use genetic algorithms, which are becoming an important tool for optimization problems, to estimate parameters in nonlinear regression models. The computational experiments indicate that this genetic based method is useful, eeective and ad...

In this study, we provide a new taxonomy of parameters of genetic algorithms (GA), structural and numerical parameters, and analyze the effect of numerical parameters on the performance of GA based simulation optimization applications with experimental design techniques. Appropriate levels of each parameter are proposed for a particular problem domain. Controversial to existing literature on GA...

From the user’s point of view, setting the parameters of a genetic algorithm (GA) is far from a trivial task. Moreover, the user is typically not interested in population sizes, crossover probabilities, selection rates, and other GA technicalities. He is just interested in solving a problem, and what he would really like to do, is to hand-in the problem to a blackbox algorithm, and simply press...