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A) (b) (d) (c) (e) Fig. 1. Different behaviors of CAs belonging to Wolfram’s Class I 50 4 Emergence, Locating and Measuring It Let first observe that there is a transient length (Tr), to be defined systematically later in Sect. 4, that differs among simulations. It can be measured as the number of iterations until the equilibrium CA state (all cells “white”) is reached. While the CA in Fig. 1a) (ID = 855) requires only a few iterations to reach the equilibrium, the longest transient (apparently associated with the most complex behavior from this class) is observed for the CA in Fig.

In practice is often more convenient to use its decimal representation, denoted ID (cell identifier). g. specifying that semitotalistic cells with nine inputs are used, or the label of a taxonomy such as “2s9” – see Sect. 7) an ID clearly individualizes a cell within a fami ly composed of all possible cells given the constraints. 3) can be implemented directly using a look-up table (a 1,024 bits R A M or R OM ) or via compact mixedsignal cells [37]. This later method is the most convenient in terms of density of cells.

Let now consider different CA behaviors from each of the classes mentioned above. First let’s start with examples belonging to Class I, as displayed in Fig. 1. Although they all fit Wolfram’s definition (the CA converges towards an equilibrium with all cells in the same state), at a closer look they exhibit different complexities and even emergence. (a) (b) (d) (c) (e) Fig. 1. Different behaviors of CAs belonging to Wolfram’s Class I 50 4 Emergence, Locating and Measuring It Let first observe that there is a transient length (Tr), to be defined systematically later in Sect.

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