

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

larger than 19 are represented, the other ones having a negligible impact on the regulation - see Eq. 1). Even with such a low number of genes, one can see that the regulatory interactions are quite complex. Gene G6 **seems to act as a central** regulatory node on the random genome, whereas that role is taken **up by** G1 in the DM-genome. Note also how few connections exist to the chosen P-genes
(G1 and G3, respectively); however, the extra protein P4 **(representing the rate of**
change of the pole, ˙θ**) is directly connected to these on both genomes. This could** very well be a mechanism for stronger reaction to changes of ˙θ**, which has been**
shown to greatly influence the success rate of a balancing attempt **(see Fig. 3).**

## 5 Conclusions

One of the main objectives of this paper was to investigate the possibility of using GRNs as a new representation for program synthesis through Genetic Programming. Our motivation was that today's mainstream Evolutionary Computation (EC) approaches are by and large crude simplifications of the biological paradigm of natural evolution, not taking into account many advances of biological knowledge in the recent past [1]. The artificial GRN model used [6] presents