Hill Climbing in Artificial Intelligence

Hill climbing algorithm is a local search algorithm that continuously moves in the direction of increasing elevation/value to find the peak of the mountain or the best solution to the problem. It terminates when it reaches a peak value where no neighbor has a higher value. The Hill climbing algorithm is a technique that is used for optimizing mathematical problems. One of the widely discussed examples of the Hill climbing algorithm is Traveling-salesman Problem in which we need to minimize the distance traveled by the salesman. It is also called greedy local search as it only looks to its good immediate neighbor state and not beyond that. Hill Climbing is mostly used when a good heuristic is available. Generate and Test variant: Hill Climbing is the variant of the Generate and Test method. The Generate and Test method produce feedback which helps to decide which direction to move in the search space. Greedy approach: Hill-climbing algorithm search moves in the direction which optimizes the cost. No backtracking: It does not backtrack the search space, as it does not remember the previous states. 🔗Important Links: Artificial Intelligence:    • Artificial Intelligence   Machine Learning:    • Machine Learning   🌐Join our community: Android App(Notes+Videos): https://play.google.com/store/apps/de... Facebook:   / thinkxacademy   Twitter:   / thinkxacademy   Instagram:   / thinkxacademy   #artificialintelligence #hillclimbing #heuristic