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Question 1 of 60 Quiz ID: 1

What is the main characteristic that distinguishes local search from backtracking state-space search?

Local search maintains paths in memory

Local search uses a single current state and moves to neighbors

Local search is always complete

Local search requires a binary goal test

Question 2 of 60 Quiz ID: 2

In which type of problems is local search particularly suitable?

Problems where the path to the goal is important

Problems requiring systematic exploration of all possibilities

Optimization problems where only the final configuration matters

Problems with small state spaces

Question 3 of 60 Quiz ID: 3

What is the primary advantage of local search algorithms?

They guarantee finding the optimal solution

They use little memory space

They are always complete

They work best for small state spaces

Question 4 of 60 Quiz ID: 4

In the 8-queens problem using local search, what does the objective function F(x) typically represent?

The number of queens on the board

The number of non-attacking pairs of queens

The number of attacking pairs of queens

The distance between queens

Question 5 of 60 Quiz ID: 5

How does the state representation differ between Chapter 3 search and Chapter 4 local search?

Chapter 3 uses complete configurations while Chapter 4 uses partial configurations

Chapter 4 uses complete configurations while Chapter 3 typically uses partial configurations

Both use the same state representation

Chapter 3 uses objective functions while Chapter 4 uses heuristic functions

Question 6 of 60 Quiz ID: 6

In the landscape visualization of local search, what does elevation correspond to?

The number of successors

The objective function value

The distance to the start state

The temperature parameter

Question 7 of 60 Quiz ID: 7

What is the fundamental operation in hill climbing (greedy local search)?

Generate all possible paths from the current state

Generate nearby successors and pick the best one

Randomly select any state from the state space

Backtrack when no improvement is found

Question 8 of 60 Quiz ID: 8

What problem does hill climbing face when it encounters a local maximum?

It will oscillate between states

It will continue searching indefinitely

It will terminate and return the local maximum

It will restart automatically

Question 9 of 60 Quiz ID: 9

What is a ridge in the context of hill climbing problems?

A flat area where all states have the same value

A region higher than its neighbors but with a slope

The global maximum of the state space

A valley between two local maxima

Question 10 of 60 Quiz ID: 10

What characterizes a plateau in hill climbing?

A steep slope leading to the global maximum

A flat area where the evaluation function is constant

A sequence of local maxima

The highest point in the state space

Question 11 of 60 Quiz ID: 11

How does stochastic hill climbing differ from basic hill climbing?

It always chooses the worst successor

It chooses randomly from among uphill moves

It uses multiple current states

It includes a temperature parameter

Question 12 of 60 Quiz ID: 12

When is first-choice hill climbing particularly useful?

When states have very few successors

When the state space is small

When a state has many (e.g., thousands) of successors

When the objective function is linear

Question 13 of 60 Quiz ID: 13

What makes random-restart hill climbing complete?

It always finds the optimal solution

It uses multiple current states simultaneously

With probability approaching 1, it will eventually generate a goal state

It never gets stuck in local maxima

Question 14 of 60 Quiz ID: 14

What is the metallurgical metaphor behind simulated annealing?

Quenching metals rapidly to increase hardness

Heating metals to high temperature then gradually cooling

Forging metals under high pressure

Alloying different metals together

Question 15 of 60 Quiz ID: 15

In simulated annealing, what happens when a generated neighbor is worse than the current state?

It is always rejected

It is always accepted

It is accepted with probability proportional to temperature and ΔE

It causes the algorithm to restart

Question 16 of 60 Quiz ID: 16

How does the temperature parameter affect moves in simulated annealing?

High T allows more worse moves, low T allows fewer worse moves

High T allows fewer worse moves, low T allows more worse moves

T only affects the speed of convergence, not move acceptance

T determines the number of restarts

Question 17 of 60 Quiz ID: 17

What is the purpose of the schedule in simulated annealing?

To determine the value of T as a function of completed cycles

To select which successors to generate

To decide when to restart the algorithm

To control the mutation rate

Question 18 of 60 Quiz ID: 18

How does local beam search differ from basic hill climbing?

It uses multiple current states instead of one

It always accepts worse moves

It uses a temperature parameter

It includes crossover operations

Question 19 of 60 Quiz ID: 19

What problem can occur in basic local beam search?

Successors can become concentrated in a small part of state space

It uses too much memory for large k values

It never converges to a solution

It requires a binary goal test

Question 20 of 60 Quiz ID: 20

How does stochastic beam search address the concentration problem?

By choosing k successors with probability proportional to their value

By increasing the number of states k

By adding a temperature parameter

By using crossover operations

Question 21 of 60 Quiz ID: 21

What is the key difference between genetic algorithms and local beam search?

Genetic algorithms use a single current state

Genetic algorithms generate successors by combining two parent states

Genetic algorithms don't use an evaluation function

Genetic algorithms are always complete

Question 22 of 60 Quiz ID: 22

In genetic algorithms, what is the fitness function?

A function that measures how fit the algorithm is

An evaluation function that measures how good a state is

A function that determines the mutation rate

A schedule for temperature reduction

Question 23 of 60 Quiz ID: 23

What is elitism in genetic algorithms?

Including top-scoring parents from previous generation

Selecting only the best individuals for reproduction

Eliminating all weak individuals

Using a high mutation rate

Question 24 of 60 Quiz ID: 24

What is culling in genetic algorithms?

Discarding individuals below a given threshold

Combining two parents to create offspring

Randomly modifying genes

Selecting individuals for reproduction

Question 25 of 60 Quiz ID: 25

What effect does crossover have in genetic algorithms?

It causes small local changes to a state

It can jump to a completely different part of the search space

It always improves the fitness of offspring

It reduces diversity in the population

Question 26 of 60 Quiz ID: 26

Which of the following is NOT a tunable parameter in genetic algorithms?

Population size

Mutation rate

Objective function

Crossover points

Question 27 of 60 Quiz ID: 27

What is a criticism of genetic algorithms mentioned in the lecture?

They use too much memory

They have many tunable parameters

They are always worse than hill climbing

They cannot solve optimization problems

Question 28 of 60 Quiz ID: 28

For which type of problems is local search NOT suitable?

Optimization problems

Problems where the path to the goal is important

Problems with large state spaces

Problems with continuous variables

Question 29 of 60 Quiz ID: 29

What is the main reason local search can handle large or infinite state spaces?

It uses little memory space

It is always complete

It guarantees optimal solutions

It uses systematic exploration

Question 30 of 60 Quiz ID: 30

In the 8-queens problem, how is a successor state typically generated?

By adding a new queen to the board

By moving one queen to a different row

By removing a queen from the board

By changing the board size

Question 31 of 60 Quiz ID: 31

What does the VALUE represent in the hill climbing algorithm pseudocode?

The number of successors

The objective function value

The temperature

The number of restarts

Question 32 of 60 Quiz ID: 32

Why is hill climbing considered incomplete?

It uses too much memory

It may get stuck in local maxima and never find the global maximum

It only works for discrete state spaces

It requires the objective function to be linear

Question 33 of 60 Quiz ID: 33

What is the purpose of the probability calculation e^(ΔE/T) in simulated annealing?

To always accept better moves

To determine the chance of accepting worse moves

To calculate the optimal temperature

To select which successor to generate

Question 34 of 60 Quiz ID: 34

As temperature decreases in simulated annealing, what happens to the probability of accepting worse moves?

It increases

It decreases

It stays the same

It becomes zero immediately

Question 35 of 60 Quiz ID: 35

In local beam search, what happens if any of the k states is a goal?

The algorithm continues searching

The algorithm halts

The algorithm restarts

The algorithm reduces k

Question 36 of 60 Quiz ID: 36

What is the typical representation of states in genetic algorithms?

As trees or graphs

As strings over a finite alphabet

As numerical vectors only

As matrices

Question 37 of 60 Quiz ID: 37

What is the role of mutation in genetic algorithms?

To combine two parents into offspring

To introduce random changes to maintain diversity

To select the best individuals

To reduce the population size

Question 38 of 60 Quiz ID: 38

What does the mixing number refer to in genetic algorithms?

The number of parents that combine to form offspring

The mutation rate

The population size

The number of generations

Question 39 of 60 Quiz ID: 39

In the N-queens example, what does the fitness value represent?

The number of attacking pairs

The number of non-attacking queens

The number of queens on the board

The number of columns with queens

Question 40 of 60 Quiz ID: 40

What is the primary advantage of genetic algorithms over hill climbing?

They use less memory

They are simpler to implement

They can find solutions that local search can't via crossover

They always converge faster

Question 41 of 60 Quiz ID: 41

What is the main disadvantage of genetic algorithms according to the lecture?

They use too much memory

They have many tunable parameters

They cannot handle large state spaces

They are always incomplete

Question 42 of 60 Quiz ID: 42

What is the termination condition for the genetic algorithm pseudocode provided?

When temperature reaches zero

When an individual is fit enough or out of time

When all possible states are generated

When the population converges to identical individuals

Question 43 of 60 Quiz ID: 43

In simulated annealing, what does ΔE represent?

The temperature change

The difference in evaluation between current and neighbor states

The energy of the current state

The schedule parameter

Question 44 of 60 Quiz ID: 44

What happens in simulated annealing when T=0?

The algorithm accepts all moves

The algorithm rejects all worse moves

The algorithm restarts

The algorithm increases temperature

Question 45 of 60 Quiz ID: 45

How does first-choice hill climbing implement stochastic hill climbing?

By always choosing the first successor generated

By generating successors randomly until finding a better one

By using a temperature parameter

By maintaining multiple current states

Question 46 of 60 Quiz ID: 46

What is the key idea behind random-restart hill climbing?

Using multiple current states simultaneously

Restarting from random positions when stuck

Adding randomness to move selection

Combining parents to create new states

Question 47 of 60 Quiz ID: 47

Why is pure random walk complete but inefficient?

It systematically explores all states

It eventually finds a solution but may take a long time

It uses optimal memory management

It always finds the shortest path

Question 48 of 60 Quiz ID: 48

What is the purpose of the schedule in simulated annealing?

To determine how temperature decreases over time

To select which successors to generate

To decide when to restart the algorithm

To control the mutation rate

Question 49 of 60 Quiz ID: 49

In genetic algorithms, what is the typical purpose of selection with replacement?

To allow very fit individuals to reproduce several times

To reduce population diversity

To eliminate weak individuals

To increase mutation rate

Question 50 of 60 Quiz ID: 50

What problem does the 'shoulder' represent in the state-space landscape?

A local maximum that is not the global maximum

A flat area that is not a peak

A ridge with difficult navigation

The global maximum of the space

Question 51 of 60 Quiz ID: 51

What is the fundamental difference between heuristic functions and objective functions?

Heuristic functions estimate distance to goal, objective functions measure state quality

Objective functions estimate distance to goal, heuristic functions measure state quality

They are identical concepts with different names

Heuristic functions are for local search, objective functions for systematic search

Question 52 of 60 Quiz ID: 52

Why might local search never explore portions of the search space where solutions exist?

Because it is not systematic

Because it uses too much memory

Because it always finds optimal solutions

Because it requires path information

Question 53 of 60 Quiz ID: 53

In the traveling salesman problem using local search, what typically represents a state?

A single city

The order of cities visited

The distance between two cities

The number of cities

Question 54 of 60 Quiz ID: 54

What is a common way to generate successor states in the traveling salesman problem?

Adding a new city to the tour

Removing a city from the tour

Changing the current ordering of cities

Changing the distance matrix

Question 55 of 60 Quiz ID: 55

What does the objective function represent in the traveling salesman problem?

The number of cities

The length of the route

The time to compute the route

The number of possible routes

Question 56 of 60 Quiz ID: 56

What is the key insight behind simulated annealing's approach to avoiding local optima?

It uses multiple current states

It occasionally accepts worse moves to escape local optima

It restarts from random positions

It uses crossover operations

Question 57 of 60 Quiz ID: 57

How does the initial temperature affect simulated annealing?

High initial temperature allows more exploration early on

Low initial temperature leads to faster convergence

Initial temperature doesn't affect performance

High initial temperature makes the algorithm greedy immediately

Question 58 of 60 Quiz ID: 58

What is the primary purpose of mutation in genetic algorithms?

To create offspring from parents

To maintain diversity in the population

To select the best individuals

To reduce the population size

Question 59 of 60 Quiz ID: 59

What is the main difference between genetic algorithms and simulated annealing?

Genetic algorithms use multiple states, simulated annealing uses one

Simulated annealing uses crossover, genetic algorithms use temperature

Genetic algorithms are always better

Simulated annealing requires more memory

Question 60 of 60 Quiz ID: 60

According to the lecture, what is the current consensus on genetic algorithms versus hill climbing with random restarts?

Genetic algorithms are always superior

Hill climbing with random restarts is always superior

No convincing evidence that GAs are generally better

They are equivalent in all cases

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