![]() 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.
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