UNISA 2024 COS3751-24-Y Welcome Message Assessment 2
QUIZ
Question 1
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What is a primary advantage of using local search algorithms for optimisation
problems?
Select one:
They provide quick solutions in large search spaces.
They guarantee to find the best global solution.
They use less memory as they store all intermediate states.
They can only solve linear problems.
They require the full problem state to be defined in advance.
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Question 2
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Which of the following is a major problem associated with local beam search?
Select one:
It lacks diversity in search paths, which can lead to premature
convergence on suboptimal solutions.
It can quickly converge to a solution without thorough exploration.
It requires extensive memory to store all potential paths.
It is computationally expensive due to the parallel processing of states.
It tends to get stuck in local optima more frequently than other search
algorithms.
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,Question 3
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In the context of local search, what is a 'plateau'?
Select one:
An area in the search space where all neighboring states have the same
value.
An area in the search space where all neighboring states have higher
value.
An area in the search space where all neighboring states have lower value.
The maximum value found during the search process.
A temporary storage used in local search algorithms.
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Question 4
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How does the temperature parameter influence the behaviour of the simulated
annealing algorithm?
Select one:
Higher temperatures increase the probability of accepting worse solutions,
aiding in escaping local optima.
Lower temperatures result in more random moves being accepted.
Temperature does not affect the solution acceptance but controls the
speed of the search.
Constant temperature ensures the algorithm behaves like a greedy
algorithm.
Temperature increases as the search progresses to explore more of the
search space.
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,Question 5
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Which of the following is an example of a local search algorithm that uses a
probabilistic move rather than a deterministic move?
Select one:
Simulated annealing
Hill climbing
Depth-first search
A* search
Greedy search
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Question 6
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What role does the k value play in a local beam search algorithm?
Select one:
It specifies the number of best successor states retained at each level of
the search.
It determines the depth of the search in each iteration.
It sets the maximum number of moves allowed in the search process.
It represents the temperature parameter similar to that in simulated
annealing.
It defines the number of parallel searches running simultaneously.
Clear my choice
Question 7
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Which variant of the hill climbing algorithm randomly selects among the uphill
moves when there is more than one?
Select one:
Stochastic Hill Climbing
Steepest-Ascent Hill Climbing
Simple Hill Climbing
Random-Restart Hill Climbing
Parallel Hill Climbing
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, Question 8
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Which of the following is typically used in a hill-climbing search algorithm?
Select one:
Heuristic to decide the next move
Backtracking
Minimax principle
Depth-first search strategy
Random restarts to avoid local maxima
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Question 9
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What is the primary characteristic of the simulated annealing algorithm that
distinguishes it from other local search algorithms?
Select one:
It uses a decreasing temperature parameter to control the probability of
making downhill moves.
It always selects the best possible move in the search space.
It uses a fixed temperature to determine the search path.
It does not allow any downhill moves during the search process.
It restarts from multiple initial states to find the best solution.
Clear my choice
Question 10
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How does local beam search utilise parallel search methods?
Select one:
By running independent searches from k different starting states without
interaction.
By synchronizing kk searches to converge at the same solution.
By distributing the kk states across multiple processors to speed up the
search.
By maintaining k iterations in parallel, each optimising a different part of
the search space.
By exploring k different paths in the search space simultaneously and
combining their results.
Clear my choice
QUIZ
Question 1
Answer saved
Marked out of 1.00
What is a primary advantage of using local search algorithms for optimisation
problems?
Select one:
They provide quick solutions in large search spaces.
They guarantee to find the best global solution.
They use less memory as they store all intermediate states.
They can only solve linear problems.
They require the full problem state to be defined in advance.
Clear my choice
Question 2
Answer saved
Marked out of 1.00
Which of the following is a major problem associated with local beam search?
Select one:
It lacks diversity in search paths, which can lead to premature
convergence on suboptimal solutions.
It can quickly converge to a solution without thorough exploration.
It requires extensive memory to store all potential paths.
It is computationally expensive due to the parallel processing of states.
It tends to get stuck in local optima more frequently than other search
algorithms.
Clear my choice
,Question 3
Answer saved
Marked out of 1.00
In the context of local search, what is a 'plateau'?
Select one:
An area in the search space where all neighboring states have the same
value.
An area in the search space where all neighboring states have higher
value.
An area in the search space where all neighboring states have lower value.
The maximum value found during the search process.
A temporary storage used in local search algorithms.
Clear my choice
Question 4
Answer saved
Marked out of 1.00
How does the temperature parameter influence the behaviour of the simulated
annealing algorithm?
Select one:
Higher temperatures increase the probability of accepting worse solutions,
aiding in escaping local optima.
Lower temperatures result in more random moves being accepted.
Temperature does not affect the solution acceptance but controls the
speed of the search.
Constant temperature ensures the algorithm behaves like a greedy
algorithm.
Temperature increases as the search progresses to explore more of the
search space.
Clear my choice
,Question 5
Answer saved
Marked out of 1.00
Which of the following is an example of a local search algorithm that uses a
probabilistic move rather than a deterministic move?
Select one:
Simulated annealing
Hill climbing
Depth-first search
A* search
Greedy search
Clear my choice
Question 6
Answer saved
Marked out of 1.00
What role does the k value play in a local beam search algorithm?
Select one:
It specifies the number of best successor states retained at each level of
the search.
It determines the depth of the search in each iteration.
It sets the maximum number of moves allowed in the search process.
It represents the temperature parameter similar to that in simulated
annealing.
It defines the number of parallel searches running simultaneously.
Clear my choice
Question 7
Answer saved
Marked out of 1.00
Which variant of the hill climbing algorithm randomly selects among the uphill
moves when there is more than one?
Select one:
Stochastic Hill Climbing
Steepest-Ascent Hill Climbing
Simple Hill Climbing
Random-Restart Hill Climbing
Parallel Hill Climbing
Clear my choice
, Question 8
Answer saved
Marked out of 1.00
Which of the following is typically used in a hill-climbing search algorithm?
Select one:
Heuristic to decide the next move
Backtracking
Minimax principle
Depth-first search strategy
Random restarts to avoid local maxima
Clear my choice
Question 9
Answer saved
Marked out of 1.00
What is the primary characteristic of the simulated annealing algorithm that
distinguishes it from other local search algorithms?
Select one:
It uses a decreasing temperature parameter to control the probability of
making downhill moves.
It always selects the best possible move in the search space.
It uses a fixed temperature to determine the search path.
It does not allow any downhill moves during the search process.
It restarts from multiple initial states to find the best solution.
Clear my choice
Question 10
Answer saved
Marked out of 1.00
How does local beam search utilise parallel search methods?
Select one:
By running independent searches from k different starting states without
interaction.
By synchronizing kk searches to converge at the same solution.
By distributing the kk states across multiple processors to speed up the
search.
By maintaining k iterations in parallel, each optimising a different part of
the search space.
By exploring k different paths in the search space simultaneously and
combining their results.
Clear my choice
