In GADO, each individual in the GA population represents a parametric description of an artifact, such as an aircraft or a missile. All parameters have continuous intervals. The fitness of each individual is based on the sum of a proper measure of merit computed by a simulator or some analysis code (such as the takeoff mass of an aircraft), and a penalty function if relevant (such as to impose limits on the permissible size of an aircraft). A steady state GA model is used, in which operators are applied to two parents selected from the elements of the population via some selection scheme, one offspring point is produced, then an existing point in the population is replaced by the newly generated point via some replacement strategy. Here selection was performed by rank because of the wide range of fitness values caused by the use of a penalty function. The replacement strategy used here is a crowding technique, which takes into consideration both the fitness and the proximity of the points in the GA population. The GA stops when either the maximum number of evaluations has been exhausted or the population loses diversity and practically converges to a single point in the search space. Floating point representation is used. Several crossover and mutation operators are used, most of which were designed specifically for the target domain type. The most innovative crossover method is guided crossover which emulates gradient based methods to improve the local convergence of the GA. GADO also uses some search control strategies such as a screening module which saves time by avoiding the full evaluation of points that are unlikely to correspond to good designs.
More about GADO can be found in my publications.