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The adaptive immune system's T and B cells can be viewed as large populationsof simple, diverse classifiers. Artificial immune systems (AIS)$\unicode{x2013}$ algorithmic models of T or B cell repertoires$\unicode{x2013}$ are used in both computational biology and natural computingto investigate how the immune system adapts to its changing environments.However, researchers have struggled to build such systems at scale. Forstring-based AISs, finite state machines (FSMs) can store cell repertoires incompressed representations that are orders of magnitude smaller than explicitlystored receptor sets. This strategy allows AISs with billions of receptors tobe generated in a matter of seconds. However, to date, these FSM-based AISshave been unable to deal with multiplicity in input data. Here, we show howweighted FSMs can be used to represent cell repertoires and model immunologicalprocesses like negative and positive selection, while also taking into accountthe multiplicity of input data. We use our method to build simpleimmune-inspired classifier systems that solve various toy problems in anomalydetection, showing how weights can be crucial for both performance androbustness to parameters. Our approach can potentially be extended to increasethe scale of other population-based machine learning algorithms such aslearning classifier systems.

Implementing Immune Repertoire Models Using Weighted Finite State Machines