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Optimizing computationally intensive models of real-world systems can be challenging, especially when significant wall clock time is required for a single evaluation of a model. Employing multiple CPUs is a common mitigation strategy, but algorithms that rely on synchronous execution of model instances can waste significant CPU cycles if there is variability in the model evaluation time. In this paper,...
FIFTHTM, a new stack-based genetic programming language, efficiently expresses solutions to a large class of feature recognition problems. This problem class includes mining time-series data, classification of multivariate data, image segmentation, and digital signal processing (DSP). FIFTH is based on FORTH principles. Key features of FIFTH are a single data stack for all data types and support for...
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