Quiz: Local and Stochastic search

Question 1

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

XABCYDEZ

Question 2

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

XABCYDEZ

Question 3

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

XABCYDEZ

Question 4

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.

Steepest-ascent hill-climbing, each time starting from a different starting stateSteepest-ascent hill-climbing, each time starting from the same starting stateStohastic hill-climbingm each time starting from a different starting stateStohastic hill-climbingm each time starting from the same starting stateBoth steepest-ascent and stohastic hill climbing, so long as you always start from the same starting stateBoth steepest-ascent and stohastic hill climbing, each time starting from a different starting stateNo version of hill-climbing will quarantee the same solution every time

Question 5

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

Steepest ascent hill-climbingRandom walkLocal beamStochastic hill-climbingSimple hill-climbingGenetic algorithmSimulated annealingFirst-choice hill-climbing

Question 6

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).

Steepest ascent hill-climbingRandom walkLocal beamStochastic hill-climbingSimple hill-climbingGenetic algorithmSimulated annealingFirst-choice hill-climbing

Question 7

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

Steepest ascent hill-climbingRandom walkLocal beamStochastic hill-climbingSimple hill-climbingGenetic algorithmSimulated annealingFirst-choice hill-climbing

Question 8

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

Steepest ascent hill-climbingRandom walkLocal beamStochastic hill-climbingSimple hill-climbingGenetic algorithmSimulated annealingFirst-choice hill-climbing

Question 9

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

Question 10

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

True

False

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