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Different regions in the state space landscape: If the function of Y-axis is Objective function, then the goal of the search is to find the global maximum and local maximum. If the function on Y-axis is cost then, the goal of search is to find the global minimum and local minimum.

On Y-axis we have taken the function which can be an objective function or cost function, and state-space on the x-axis. The state-space landscape is a graphical representation of the hill-climbing algorithm which is showing a graph between various states of algorithm and Objective function/Cost. No backtracking: It does not backtrack the search space, as it does not remember the previous states.

Greedy approach: Hill-climbing algorithm search moves in the direction which optimizes the cost.

The Generate and Test method produce feedback which helps to decide which direction to move in the search space.

Generate and Test variant: Hill Climbing is the variant of Generate and Test method.

In this algorithm, we don't need to maintain and handle the search tree or graph as it only keeps a single current state.įollowing are some main features of Hill Climbing Algorithm:.

Hill Climbing is mostly used when a good heuristic is available.

A node of hill climbing algorithm has two components which are state and value.

It is also called greedy local search as it only looks to its good immediate neighbor state and not beyond that.

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.

Hill climbing algorithm is a technique which is used for optimizing the mathematical problems.

It terminates when it reaches a peak value where no neighbor has a higher value.

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.Next → ← prev Hill Climbing Algorithm in Artificial Intelligence

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