Mesoscopic analysis of self-evolution in an artificial chemistry

Dittrich, Peter, Jens Ziegler, and Wolfgang Banzhaf. “Mesoscopic analysis of self-evolution in an artificial chemistry.” In Artificial Life VI , pp. 95-103. 1998.

In an algorithmic articial chemistry the objects (molecules) are data and the interactions (reactions) among them are dened by an algorithm. The same object can appear in two forms: (1) as a machine (operator) or (2) as data (operand). Thus, the same object can, on the one hand, process other objects or, on the other hand, it can be processed. This dualism enables to implicitly dene a constructive articial chemistry which exhibits quite complex behavior. Remarkably, even evolutionary behavior emerged in our experiments, without dening any explicit variation operators or tness-function. In addition to microscopic methods (e.g., monitoring the actions of single molecules) and macroscopic measures (e.g., diversity or complexity) we developed a stepwise mesoscopic analysis method based on classication and dynamic clustering. Knowledge about the system is accumulated by an iterative process in which measuring tools (classicators) extract information which in turn is used to create new classicators.

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