![]() ![]() 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. ![]()
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