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Extra info for Advances in Artificial Life: 7th European Conference, ECAL 2003, Dortmund, Germany, September 14-17, 2003. Proceedings
2, . Let us denote the number of vertices by n, and let m be the number of edges of the substrate graph of a chemical reaction network. In the context of analyzing large networks it has turned out that the following measures yield useful characteristics: The average node degree k = 2 m n. g. . Let dx be the degree of a vertex x and let qx be the number of edges that connect neighbors of a vertex x. , Cx = 2qx /((dx (dx − 1))). The clustering coeﬃcient C then measures the overall cliquishness of the graph .
Correspondingly the entries (x, r) in the experience database and the expectations (x, r ) formed for a location prior to its sampling are such vectors (cf. Fig. 1). The surprise value d, which constitutes the ﬁtness criterion, is computed as the Shannon entropy  of the diﬀerence between the actual response r and response that was expected r . The expectation r for a location x = (x, y) is computed as distance-weighted average over the (up to) 25 measurements nearest to x available in the experience database.
We start the construction of RESAC, however, by deﬁning the third component, the AC algorithm: The algorithm A driving the reactor M models a well-stirred reaction vessel (see Fig. 2). Thus arbitrary molecules can collide at any time. What seems to be an artiﬁcial simpliﬁcation is, however, a concept of chemistry  if parameters like, for example, speed of stirring are chosen appropriately. Additionally, new start-clauses can feed the reactor—the so-called inﬂow—at a certain rate (inﬂow rate).