THE CONTROLLED RANDOM SEARCH ALGORITHM IN OPTIMIZING REGRESSION-MODELS

This paper deals with the problems of controlled random search algorithms (CRS algorithms) and their use in regression analysis. A modified CRS algorithm of Price is described, which is more effective when compared with the original algorithm in optimizing regression models, first non-linear ones. The principal modification consists in randomizing the search for the next trial points. Some results of testing the algorithm, using both real and modeled data, are given to illustrate its possibilities when estimating the parameters of non-linear regression models.