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Artificial Intelligence Practice Test: Local Search and Optimization Problems
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In computer science, local search is a heuristic method for solving computationally hard optimization problems. Local search can be used on problems that can be formulated as finding a solution maximizing a criterion among a number of candidate solutions.

Artificial Intelligence Practice Test: Local Search and Optimization Problems
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12 Questions

1. Which of the following algorithm maintains track of k states instead of just one.
2. Hill climbing is also called which of the following ………. local search because it takes a good neighbor state without thinking ahead about where to go next.
3. The Hill-Climbing technique stuck for some reasons. which of the following is the reason?
4. Which of the following are the two key characteristics of the Genetic Algorithm?
5. In many cases the path to the goal is not related, this class of problems can be solved using which of the following Techniques?
6. According to which of the following algorithm, a loop that continually moves in the direction of increasing value, that is uphill.
7. Though local search algorithms are not systematic, vital advantages would include which of the following?
8. When will the Hill-Climbing algorithm terminate?
9. Which of the following are the key shortcomings of the hill-climbing search?
10. A complete, local search algorithm always tries to find the goal if one exists, an optimal algorithm always tries to finds a global minimum/maximum.
11. Searching using a query on the Internet is, use of which of the following type of agent?
12. A genetic algorithm is a variant of stochastic beam search and in this, we can combine two-parent states to generate successor states, instead of altering a single state.

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