On Parameter Adjustment of the Immune Inspired Machine Learning Algorithm AINE.
Kent University, Canterbury, Kent. CT2 7NF.
A machine-learning algorithm based on the natural immune system metaphor has been developed, AINE (Artificial Immune Network). AINE developed from initial work on Artificial Immune Systems for data analysis, for which detailed experimentation was undertaken as to the affect of altering algorithm parameters had on the behaviour of the system. Two of the parameters, the network affinity threshold and mutation rate have been carried over into the new version, AINE. A third parameter the number of resources has been introduced into AINE as a means by which to control network size and create a stable network structure. This paper provides details of experiments, which alter these three parameters in AINE. It was expected that the two parameters taken from the AIS would, when altered, exhibit the same behaviour in AINE, that being the NAT scalar affecting network connectivity and mutation rate affecting network size and connectivity. Indeed, this was found to be the case. The third parameter, was designed to create stable network and previous work has shown this to be the case. It is shown in this paper, that the esource parameter can be used to control population size within the network.
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