where yt = f(xt**). The t-test statistics of the significance measure are given**
below the coefficients in parentheses: they are all significant.

Diagnostic checking For the ARMA(2, **1) model estimation, the Akaide**
Information Criterion (aic) is −16763.**63 and the variance of residuals is**
V ar(ǫt) = 0.**01094. Figure 17 shows the residuals autocorrelation and p-value**
of Box-Jenkins test for the estimates of the ARMA(2, **1) model. The acf of** residuals are all well within the two-standard-error bound expected for h **= 28.**
So, the residuals are not correlated. The p-value of Box-Jenkins test are quite good over 0.**25. The residuals can be considered as white noises.**
The value of R-square R¯2 = 0.**7050 is high and higher in comparison to the**
synchronizing-CA problem [11] where R¯2is equal to 0.38 and 0.35.

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

We can conclude that the model ARMA(2, **1) accurately fits the fitness values given by random walks over the Olympus Landscape. The high correlation**
shows that a local search heuristic is adequate to find good rules on **the Olympus. An autoregressive model of size 2 means that we need two steps to predict**
the fitness value; so, as suggested by Hordijk, it would be possible to construct more efficient local search taking into account this information. The **moving** average component has never been found in other landscape fitness analysis. What kind of useful information does it give? Maybe information on nature of neutrality. Future work should study those models in more detail.

## 5.2.4 Fitness Cloud And Nsc

Figure 18 shows the scatterplot and the segments {S1, S2, ..., Sm−1} **used to**
calculate the NSC on the Olympus (see section 4.1). No segment has a **negative**