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This paper describes the automatic discovery, via an Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), of vectors of real-valued coefficients representing matched forward and inverse transforms that outperform the 9/7 Cohen-Daubechies-Feauveau (CDF) discrete wavelet transform (DWT) for satellite image compression and reconstruction under(More)
We present a genetic classifier system approach to the text-independent open-set speaker identification problem. Classifier systems are widely used in symbolic problem for dynamically changing open-ended learning. Signal processing problems require processing of real-valued parameters that classifier systems are not designed for. On the other hand, the(More)
This paper presents the design and implementation of an <i>adaptive open-set speaker identification system</i> 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(More)
We consider the future cyber security of industrial control systems. As best as we can see, much of this future unfolds in the context of the Internet of Things (IoT). In fact, we envision that all industrial and infrastructure environments, and cyber-physical systems in general, will take the form reminiscent of what today is referred to as the IoT. IoT is(More)
We present the design and implementation of an open-set text-independent speaker identification system using genetic learning classifier systems (LCS). We examine the use of this system in a real-number problem domain, where there is strong interest in its application to tactical communications. We investigate different encoding methods for representing(More)
Machine learning is rapidly emerging as a valuable technology thanks to its ability to learn patterns from large data sets and solve problems that are impossible to model using conventional programming logic. As machine learning techniques become more mainstream, they are being applied to a wider range of application domains. These algorithms are now(More)
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