Optimization is concerned with the finding of global optima (hence the
name) of problems that can be cast in the form of a function of
several variables and constraints thereof. Among the searching
methods, {\em Evolutionary Algorithms} have been shown to be adaptable and
general tools that have often outperformed traditional {\em ad hoc}
methods. The {\em Breeder Genetic Algorithm} (BGA) combines a direct
representation with a nice conceptual simplicity. This work contains a
general description of the algorithm and a detailed study on a collection of
function optimization tasks. The results show that the BGA is a
powerful and reliable searching algorithm. The main discussion
concerns the choice of genetic operators and their parameters, among
which the family of Extended Intermediate Recombination (EIR) is shown
to stand out. In addition, a simple method to dynamically adjust the
operator is outlined and found to greatly improve on the already
excellent overall performance of the algorithm.