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: