What are the main cons of hill-climbing search? A. Terminates at local optimum & Does not find optimum solution B. Terminates at global optimum & Does not find optimum solution C. Does not find optimum solution & Fail to find a solution D. Fail to find a solution

The correct answer is: A. Terminates at local optimum & Does not find optimum solution.

Hill climbing is a simple search algorithm that works by iteratively moving to the neighboring state that has the best value. This process continues until a local optimum is reached, which is a state that cannot be improved upon by moving to any

While hill climbing is a simple and efficient algorithm, it has a number of limitations. One limitation is that it can only find local optima, which may not be the global optimum. Another limitation is that it can get stuck in local optima, which can happen if the search space is large or if the starting state is not chosen carefully.

Here is a brief explanation of each option:

• Option A: Terminates at local optimum & Does not find optimum solution. This is the main limitation of hill climbing. As mentioned above, hill climbing can only find local optima, which may not be the global optimum.
• Option B: Terminates at global optimum & Does not find optimum solution. This is not a limitation of hill climbing. If the search space is small and the starting state is chosen carefully, hill climbing can find the global optimum.
• Option C: Does not find optimum solution & Fail to find a solution. This is not a limitation of hill climbing. If the search space is large or the starting state is not chosen carefully, hill climbing may not find the global optimum, but it will always find a solution.
• Option D: Fail to find a solution. This is a limitation of hill climbing. If the search space is large or the starting state is not chosen carefully, hill climbing may not find a solution.