Emergent Behaviour in the Frequency-Power Spectrum of Discrete Dynamic Networks
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In the fields of cellular automata and complex systems, emergence isoften used as an interpretation of system behaviour. Computation and theresulting output are both products of the systems trajectory in the basinof attraction, where the output data is a point or cyclic attractor. Assuch, the system outputs only a single variable, the states of all unitsin the system. This work diverges from the norm on two aspect. Instead ofexploring Cellular Automata as the computational architecture, BooleanNetworks, a specialisation of the more generalised Discrete DynamicNetworks, will be used. Secondly, a different approach in interpretingthe behaviour is taken. Instead of looking directly at the state of thesystem, the trajectory in the basin of attraction is instead transformedto a frequency-power spectrum representing the system output. This allowsan easy interpretation of the output (peeks) to several output variables,were each variables can be given as the power at different frequenciesin the frequency-power spectrum.Because of the difficulty in programming, i.e. designing, Discrete DynamicNetworks with the desired characteristics, a Genetic Algorithm will beused to evolve the networks. This thesis takes an experimental approach,evolving Discrete Dynamic Networks capable of producing differentnumber of peaks in the frequency-power spectrum. The results show thatDiscrete Dynamic Network exhibiting the desired emergent behaviour weresuccessfully evolved.