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Evolutionary Computation refers to computational models that simulate natural evolution processes, such as selection, mutation, reproduction and recombination, to solve optimization problems. It is a machine learning approach inspired by biological evolution, where solutions evolve over time through successive generations. Algorithms in this field, like Genetic Algorithms, Genetic Programming, Evolutionary Strategies, and others, operate on populations of candidate solutions and apply genetic operators to find increasingly fitter individuals. These methods have been widely applied to various domains including decision support systems, where they help solve complex problems by searching large solution spaces efficiently. They contribute to technology advancements in AI algorithms, enabling more robust, adaptive and efficient decision making processes.