HILL CLIMBING ALGORITHM IN ARTIFICIAL INTELLIGENCE | CSE&IT TUTORIAL

Hill Climbing Algorithm in Artificial Intelligence Hill climbing algorithm is a local search algorithm which continuously moves in the direction of increasing elevation/value to find the peak of the mountain or best solution to the problem. It terminates when it reaches a peak value where no neighbor has a higher value. Hill climbing algorithm is a technique which is used for optimizing the mathematical problems. One of the widely discussed examples of Hill climbing algorithm is Traveling-salesman Problem in which we need to minimize the distance traveled by the salesman Features of Hill Climbing: Generate and Test variant Greedy approach No backtracking Different regions in the state space landscape: Local Maximum Global Maximum Current state Flat local maximum shoulder Types of Hill Climbing Algorithm: Simple hill Climbing: Steepest-Ascent hill-climbing: Stochastic hill Climbing: Problems in Hill Climbing Algorithm 1. Local Maximum: 2. Plateau: 3. Ridges: