A genetic optimization computer system comprises a model and an optimizer. The model defines the structure of a candidate solution to a problem as a plurality of objects in combination (A,B,C). The objects consist of defined parameters (x,y). The model also runs potential solutions to the problem and generates an output. The optimizer stores potential solution candidates and crosses pairs of them to produce new child solution candidates which are run by the model. The child solutions are evaluated on the basis of the model output and their fitness for purpose indicated, and identified to the optimizer. The model also defines at least one group of objects which are identically structured and equivalent to each other. By associating each object of the defined group from one solution candidate with an object of the defined group from another solution candidate so as to minimize the difference between the respective groups prior to crossing the candidates, a faster convergence towards an optimum solution is achieved.