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- DATA.ML.310
- 3. Classical Search and Beyond
- 3.3 Quiz: Local and Stochastic search

# Quiz: Local and Stochastic search¶

Consider running simple hill climbing search on the graph shown above. If we start from position X, we will end up at

Consider running simple hill climbing search on the graph shown above. If we start from position Y, we will end up at

Consider running simple hill climbing search on the graph shown above. If we start from position Z, we will end up at

For which of the following will you always find the same solution, even if you re-run the algorithm multiple times?
Assume a problem where the goal is to minimize a cost function.

Give the name of the algorithm that results from the following special case. Local beam search with k = 1.

Give the name of the algorithm that results from the following special case. Simulated annealing with T = 0 at all times (and omitting the termination test).

Give the name of the algorithm that results from the following special case. Simulated annealing with T = ∞ at all times.

Give the name of the algorithm that results from the following special case. Genetic algorithm with population size N = 1.

Arrange the following options to represent the correct steps of a simple genetic algorithm. Type letters in the correct order without commas or periods. For example a correctly formatted answer would be abcde

a. Select Parents based on their fitness

b. Determine population fitness

c. Mutation

d. Initialize population

e. Crossover

True or False: A simple hill-climbing search can never reach global optimum of a function with multiple local optima.

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