that are close to these unsolvable cases. It is interesting to see however how the

![0_image_0.png](0_image_0.png)

![0_image_1.png](0_image_1.png)

unsolved cases of the DM-genome are mostly symmetric in terms of the matrix of test cases, whereas the random genome is far more unbalanced. This has to do with the sinusoidal nature of the controllers generated by random genomes, as can be seen in the next section.

## 4.2 Pole Balancing Behaviour Of Typical Networks

Fig. 4 shows example behaviours of the 2 best evolved regulatory models (random and DM, using P-protein concentrations), applied to 3 different generalisation cases.

It is interesting to observe the different approaches to solve the same generalisation test. In particular, one can see how the random genome is quite sinusoidal in its approach, whereas the DM-genome generates a much more linear behaviour.

## 4.3 Resulting Networks: A Typical Example

Fig. 5 shows the regulatory networks extracted from the best performing random and DM-genomes, at a threshold of 19 (i.e. only connections with a match