meta-heuristics

Meta-heuristics are high-level procedures or strategies designed to guide underlying heuristics toward a more effective search for solutions to complex optimization problems. They are particularly useful in scenarios where traditional optimization methods may fail due to the size or complexity of the search space. Common examples include genetic algorithms, simulated annealing, and tabu search.