Artificial Life VI


Mesoscopic Analysis of Self-Evolution in an Artificial Chemistry

Peter Dittrich
University of Dortmund, Dept. of Computer Science, D-44221 Dortmund, Germany
http://ls11-www.informatik.uni-dortmund.de/people/dittrich

Jens Ziegler
University of Dortmund, Dept. of Computer Science, D-44221 Dortmund, Germany
http://ls11-www.informatik.uni-dortmund.de/people/ziegler

Wolfgang Banzhaf
University of Dortmund, Dept. of Computer Science, D-44221 Dortmund, Germany
http://ls11-www.informatik.uni-dortmund.de/people/banzhaf


Abstract

In an algorithmic artificial chemistry the objects (molecules) are data and the interactions (reactions) among them are defined 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 define a constructive artificial chemistry which exhibits quite complex behavior. Remarkably, even evolutionary behavior emerged in our experiments, without defining any explicit variation operators or fitness-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 classification and dynamic clustering. Knowledge about the system is accumulated by an iterative process in which measuring tools (classificators) extract information which in turn is used to create new classificators.


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