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This is an introduction to the concept of symbolic regression for managing effectively data stream. Symbolic regression combines genetic algorithm, reinforcement learning and flexible policies to extract meaning or knowledge from data, in an ever changing environment. As the knowledge extracted from real-time data is human readable and consumable, decision makers can validate the findings of the algorithm and act appropriately. Symbolic regression is used in signal processing, process monitoring and adaptive caching in data centers.
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