Signal processing problems including the speaker identification problem require processing of real-valued feature vectors. Traditional cepstral encoding combined with clustering algorithms
handle the closed-set speaker identification problem quite well but when it comes to the open-set problem, clustering methods show lack of performance. Furthermore, many clustering algorithms lack adaptability and the ability to learn on-the-fly. Genetic classifier systems are adaptive and they have the ability for open-ended learning. We introduce a genetic classifier system approach to the speaker identification problem and several classifier knowledge representation methods for open-set speaker identification. Experimental results show that the new system works quite well for the open-set speaker identification problem.
This paper discusses how evolutionary algorithms are related to multi-agent systems and the possibility of military applications using the two disciplines. In particular, we present a game theoretic model for multi-agent resource distribution and allocation where agents in the environment must help each other to survive. Each agent maintains a set of variables representing actual friendship and perceived friendship. The model directly addresses problems in reputation management schemes in multi-agent systems and Peer-to-Peer distributed systems. We present algorithms based on evolutionary game process for maintaining the friendship values as well as a utility equation used in each agent's decision making. For an application problem, we adapted our formal model to the military coalition support problem in peace-keeping missions. Simulation results show that efficient resource allocation and sharing with minimum communication cost is achieved without centralized control.
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