This paper presents the design and implementation of an adaptive open-set speaker identification system with genetic learning classifier systems. One of the challenging problems in using learning classifier systems for numerical problems is the knowledge representation. The voice samples are a series of real numbers that must be encoded in a classifier format. We investigate several different methods for representing voice samples for classifier systems and study the efficacy of the methods. We also identify several challenges for learning classifier systems in the speaker identification problem and introduce new methods to improve the learning and classification abilities of the systems. Experimental results show that our system successfully learns 200 voice features at the accuracies of 60% to 80%, which is considered a strong result in the speaker identification community. This research presents the feasibility of using learning classifier systems for the speaker identification problem.