CHAPTER 4 | sammour

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Multiple choice questions:

1. What is the difference between the search problems discussed in Chapter 3 and those discussed in Section 4.1? a) Chapter 3 deals with optimization problems, while Section 4.1 deals with finding a good final state without worrying about the path to get there. b) Chapter 3 deals with finding a good final state without worrying about the path to get there, while Section 4.1 deals with optimization problems. c) There is no difference between the two, they both deal with finding a path through a search space.

2. What is the advantage of local search algorithms? a) They are systematic and explore the entire search space. b) They use very little memory. c) They can only be used for small state spaces.

True or false questions:

1. Local search algorithms keep track of the paths and set of states that have been reached.
2. Local search algorithms are suitable for large or infinite state spaces where systematic algorithms are not.
3. Local search algorithms can only find valid final configurations of a problem, not optimize a solution.

Multiple choice questions:

1. a) Chapter 3 deals with optimization problems, while Section 4.1 deals with finding a good final state without worrying about the path to get there.
2. b) They use very little memory.

True or false questions:

1. False
2. True
3. False صفحة ٢

True or false questions:

1. Hill climbing search keeps track of all states in the search space.
2. Hill climbing search aims to find the lowest valley in a landscape.
3. Hill climbing search terminates when it reaches a peak with no higher value neighbors.
4. Hill climbing search always finds the global maximum.

Multiple choice questions:

1. What is the objective function for hill climbing when elevation corresponds to cost? a) Find the highest peak. b) Find the lowest valley. c) Use the negative of a heuristic cost function.
2. What does hill climbing search do on each iteration? a) It moves to a random neighbor state. b) It moves to the neighboring state with the highest value. c) It looks ahead beyond the immediate neighbors of the current state.

True or false questions:

1. False
2. False
3. True
4. False

Multiple choice questions:

1. c) Use the negative of a heuristic cost function.
2. b) It moves to the neighboring state with the highest value. صفحة ٣ Multiple choice questions:
3. What is the heuristic cost function for the 8-queens problem? a) The number of queens in the right place. b) The number of moves required to reach a solution. c) The number of pairs of queens attacking each other.
4. What is a local maximum in hill climbing? a) A peak that is higher than each of its neighboring states but lower than the global maximum. b) A peak that is higher than both its neighboring states and the global maximum. c) A valley that is lower than each of its neighboring states but higher than the global minimum.

True or false questions:

1. Hill climbing is sometimes called greedy local search because it looks ahead to plan its next move.
2. Local maxima are peaks that are higher than the global maximum.
3. Hill climbing can get stuck in local maxima or ridges.

Multiple choice questions:

1. c) The number of pairs of queens attacking each other.
2. a) A peak that is higher than each of its neighboring states but lower than the global maximum.

True or false questions:

1. False
2. False
3. True

Questions:

1. What is a local maximum in hill climbing? a) A peak that is higher than both its neighboring states and the global maximum. b) A peak that is higher than each of its neighboring states but lower than the global maximum. c) A valley that is lower than each of its neighboring states but higher than the global minimum.
2. What is a plateau in hill climbing? a) A flat local maximum, from which no uphill exit exists. b) A flat area of the state-space landscape, which can be a flat local maximum or a shoulder. c) A sequence of local maxima that is very difficult for greedy algorithms to navigate.
3. What is the simulated annealing algorithm? a) A hill-climbing algorithm that combines hill climbing with a random walk. b) An algorithm that uses natural selection in biology to produce offspring states. c) An algorithm that starts by shaking hard and gradually reduces the intensity of the shaking.
4. What is the local beam search algorithm? a) An algorithm that keeps track of k states rather than just one. b) An algorithm that selects the k best successors from the complete list at each step. c) An algorithm that chooses successors with probability proportional to the successor's value.

True or false questions:

1. Hill climbing can get stuck in local maxima, ridges, and plateaus.
2. Local beam search keeps track of only one state.
3. Stochastic beam search chooses successors with probability proportional to the successor's value to increase diversity.
4. Evolutionary algorithms are variants of hill climbing that combine hill climbing with a random walk.

Questions:

1. b) A peak that is higher than each of its neighboring states but lower than the global maximum.
2. b) A flat area of the state-space landscape, which can be a flat local maximum or a shoulder.
3. c) An algorithm that starts by shaking hard and gradually reduces the intensity of the shaking.
4. a) An algorithm that keeps track of k states rather than just one.

True or false questions:

1. True
2. False
3. True
4. False

True or false questions:

1. True or False: The size of the population is a factor that affects the performance of evolutionary algorithms.
2. True or False: In genetic algorithms, each individual is represented by a sequence of real numbers.
3. True or False: The mixing number, ρ, represents the number of offspring produced by two parents.
4. True or False: One selection process for choosing parents is to select individuals with probability proportional to their fitness score.
5. True or False: Recombination in genetic algorithms involves randomly selecting a crossover point and swapping the parts of the parent strings.
6. True or False: The mutation rate determines how likely it is for offspring to have random mutations in their representation.
7. True or False: Elitism is a practice where top-scoring parents from the previous generation are included in the next generation.

Multiple choice questions:

1. What is the most common representation of an individual in genetic algorithms? a. Sequence of real numbers b. Boolean string c. Computer program

2. What is the mixing number, ρ, in evolutionary algorithms? a. The number of individuals in the population. b. The number of parents that come together to form offspring. c. The number of offspring produced by two parents.

3. What is one possible selection process for choosing parents in evolutionary algorithms? a. Randomly selecting individuals with equal probability. b. Selecting individuals with probability proportional to their fitness score. c. Selecting the ρ most fit individuals from a group of randomly selected individuals.

4. What does recombination involve in genetic algorithms? a. Randomly selecting a crossover point and swapping the parts of the parent strings. b. Mutating every bit in the composition of an offspring. c. Selecting the ρ most fit individuals as parents for the next generation.

5. What is elitism in evolutionary algorithms? a. The practice of randomly selecting parents for the next generation. b. The practice of including top-scoring parents from the previous generation in the next generation. c. The practice of discarding all individuals below a given fitness threshold.

True or false questions:

1. True
2. False
3. False
4. True
5. True
6. True
7. True

Multiple choice questions:

1. b. Boolean string
2. b. The number of parents that come together to form offspring.
3. b. Selecting individuals with probability proportional to their fitness score.
4. a. Randomly selecting a crossover point and swapping the parts of the parent strings.
5. b. The practice of including top-scoring parents from the previous generation in the next generation.