Using GP and Cultural Algorithms to Simulate the Evolution of an Ancient Urban Center

Numerous models of modern and ancient urban landscapes have been proposed. While is of interest to classify examples of early urban centers, it is even more interesting to model their origins. Since these emergent centers can be viewed not only as adaptations to their social and biological environments, but also as a source of further change. Thus, the meaning or semantics of an emergent center reflects the processes by which it was formed. In the study of one ancient center, Monte Albán, we have used data mining techniques to extract a large number of decision trees describing its settlement over time. Each decision tree specifies the values for selected attributes that can predict settlement activity in the terraces that comprise the site. However, not all terraces with those properties are occupied in the same way. This can result from economic or social reasons. Also, the variables employed do not always have the semantics needed to make sense of the distinction between occupied and unoccupied terraces. In this paper we focus on the latter reason. Here, GP and CA are used to add semantics to rules to make them more understandable to experts in the area. Future work will examine how GP can be used to integrate social and economic constraints in with the basic decision tree rules.