Each time the program is run the genetic operations determine the creation of a different object. By observing the result and analysing the genetic data, it becomes possible to perform a manual evolution of the original chromosome pool. From the initial state complete variation, artificial evolution (design oriented) of the objects created by the genetic lab can be achieved using three different procedures:

- Editing the content of the chromosome pool I change the values that give poor results, or introduce new values to test the result.

- Editing the content of the library I replace structure or shape functions that don't give interesting results by new ones.

- Editing the selection function I can override the genetic data from a chromosome, or limit the objects from the library that can actually be selected.

This way I have the possibility, following to some design idea, to lead the genetic lab into producing more interesting objects.

Images captured from the genetic lab program:

The operation of evolving the genetic lab objects still requires some persistence, because the selection of chromosomes is random and there is a wide variety of chromosomes and elements in the library. The reason for this lies in the origin of this program: the City Generator. There a wide range of objects is required to achieve diversity, so this program also points towards a wide range of results, and not to a convergence into a single optimum form.

To optimise the program for such a design operation the library of structures and shapes should be reduced and contain only elements that are close to the desired result. The fact that the structure algorithms are closed is also a drawback. The elements from the library have to be designed and only some parameters are changed by the chromosome data. Ideally the algorithms from the library should be cut into elementary pieces that could be exchanged, to produce more unexpected combinations by creating new algorithms.