Metaheuristic
Metaheuristic is a problem-solving approach that uses high-level strategies to find, generate, or select heuristics that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity. Metaheuristics are often used in data science and artificial intelligence to solve complex problems that cannot be solved by exact methods or traditional algorithms. Metaheuristics can be applied to a wide range of problems, such as clustering, classification, regression, feature selection, dimensionality reduction, and hyperparameter tuning. Some popular metaheuristic algorithms include genetic algorithms, simulated annealing, particle swarm optimization, ant colony optimization, and tabu search.
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