Table 2 ![0_image_0.png](0_image_0.png)

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

Computational costs do not allow us to analyze many neutral networks. In this section we analyze two important large neutral networks (NN**). A large number of CAs solve the majority density problem on only half of ICs because they**
converge nearly always on the final configuration (O)
N or (1)N **and thus have**
performance about 0.**5. Mitchell [5] calls these "default strategies" and notices**
that they are the first stage in the evolution of the population before jumping to higher performance values associated to "block-expanding" strategies (see section 2.3). We will study this large NN, denoted NN0.5 **around standard**
performance 0.5 to understand the link between NN properties and GA evolution. The other NN, denoted NN0.76, is the NN around fitness 0.**7645 which**
contains one neighbor of a CA found by Mitchell et al**. The description of this** "high" NN could give clues as how to "escape" from NN **toward even higher** fitness values.

In our experiments, we perform 5 neutral walks on NN0.5 **and 19 on** NN0.76.

Each neutral walk has the same starting point on each NN**. The solution** with performance 0.**5 is randomly solution and the solution with performance**