Give the name of the algorithm that results from each of the following special cases: a. Local beam search with k = 1. b. Local beam search with one initial state and no limit on the number of states retained. c. Simulated annealing with T = 0 at all times (and omitting the termination test). d. Simulated annealing with T = ∞ at all times. e. Genetic algorithm with population size N = 1.

Question

Want to see more full solutions like this?

Answer 1

Textbook Question

Chapter 4, Problem 1E

Give the name of the algorithm that results from each of the following special cases:

a. Local beam search with k = 1.
b. Local beam search with one initial state and no limit on the number of states retained.
c. Simulated annealing with T = 0 at all times (and omitting the termination test).
d. Simulated annealing with T = ∞ at all times.
e. Genetic algorithm with population size N = 1.

Expert Solution & Answer

Explanation of Solution

a.

The local beam search with “k=1” is hill-climbing search.

b.

Local beam search with one initial state and no limit on the number of states retained resembles with Breadth-First search.
In breadth first search, before adding the next layer it adds one complete layer nodes.
Starting from one state, the algorithm would be essentially identical to breadth-ﬁrst search except that each layer is generated all at once.

c.

Simulated annealing with “T=0” at all time:

There is a fact that termination step would be triggered immediately. Ignoring this fact, the search would be identical to first choice hill climbing.
This is because; every downward successor would be rejected with probability 1.

d.

Simulated annealing with “T = ∞” at all times is a random-walk search, it always accepts a new state.

e.

Generic algorithm with population size “N=1”:

The two selected parents will be same individual, if the population size is “1”.
The crossover yields an exact copy of individuals. Here, the mutation chance occurs.
Thus, the algorithm executes a random walk in the space of individuals.

Want to see more full solutions like this?

Subscribe now to access step-by-step solutions to millions of textbook problems written by subject matter experts!

05:06

Students have asked these similar questions

Note: Answer the question using Java language only. Shaker is the first child who got scholarship into the village. He went to London to study where he finds it very interesting to calculate number of ways of going to point (c, d) from point (a, b) in co-ordinate plane. We can take horizontal and vertical steps only and cannot visit at a point twice. In a step, you can move one unit only. We have to reach to the point (c, d) from the point (a, b) using abs(a-c) + abs(b-d) steps only. Shaker has two sets of points. Set A contains points having X co- ordinate 0 and Y co-ordinates varying from 1 to N (both inclusive). Set B contains points having X co-ordinate K and Y co-ordinates varying from 1 to N (both inclusive). Both sets contain N number of integral points. He wants to calculate the sum of number of ways to going to each point of set B from each point of set A. Input 1 22 Output 8

In practical life, the employees get salaries and pay taxes honestly. Sometimes, the process of drawing salariesand payment of taxes may lead to some interesting situation. Suppose, a person draws salary of Rs. 10,000 permonth. A certain percentage of tax is charged on that amount, which is deducted every month. But if the salaryof the person is more than Rs. 10,000 per month, then the tax rate is different. Similarly if a person is getting Rs.20,000 per month, he/she would be charged more under a different tax rate slab. The interesting situationdevelops if there is an anomaly in the tax rates i.e. a person who is getting higher salary takes home lesser moneyas compared to the other person with less gross salary.To further elaborate it, we suppose that there is company 'C' where 100 or less than 100persons are employed. The salaries of the employees and their tax rates are known to us.We are required to list those unlucky persons, who are getting lesser take-home salary(net salary)…

Correct answer will be upvoted else Multiple Downvoted. Don't submit random answer. Computer science. You have n particular focuses (x1,y1),… ,(xn,yn) on the plane and a non-negative integer boundary k. Each point is a tiny steel ball and k is the draw in force of a ball when it's charged. The draw in power is something very similar for all balls. In one activity, you can choose a ball I to charge it. When charged, all balls with Manhattan distance all things considered k from ball I move to the situation of ball I. Many balls may have a similar facilitate after an activity. All the more officially, for all balls j with the end goal that |xi−xj|+|yi−yj|≤k, we dole out xj:=xi and yj:=yi. An illustration of an activity. Subsequent to charging the ball in the middle, two different balls move to its position. On the right side, the red dab in the middle is the normal situation of those balls. Your errand is to observe the base number of activities to move all balls to a similar…

Answer 2

Textbook Question

Chapter 4, Problem 1E

Give the name of the algorithm that results from each of the following special cases:

a. Local beam search with k = 1.
b. Local beam search with one initial state and no limit on the number of states retained.
c. Simulated annealing with T = 0 at all times (and omitting the termination test).
d. Simulated annealing with T = ∞ at all times.
e. Genetic algorithm with population size N = 1.

Expert Solution & Answer

Explanation of Solution

a.

The local beam search with “k=1” is hill-climbing search.

b.

Local beam search with one initial state and no limit on the number of states retained resembles with Breadth-First search.
In breadth first search, before adding the next layer it adds one complete layer nodes.
Starting from one state, the algorithm would be essentially identical to breadth-ﬁrst search except that each layer is generated all at once.

c.

Simulated annealing with “T=0” at all time:

There is a fact that termination step would be triggered immediately. Ignoring this fact, the search would be identical to first choice hill climbing.
This is because; every downward successor would be rejected with probability 1.

d.

Simulated annealing with “T = ∞” at all times is a random-walk search, it always accepts a new state.

e.

Generic algorithm with population size “N=1”:

The two selected parents will be same individual, if the population size is “1”.
The crossover yields an exact copy of individuals. Here, the mutation chance occurs.
Thus, the algorithm executes a random walk in the space of individuals.

Want to see more full solutions like this?

Subscribe now to access step-by-step solutions to millions of textbook problems written by subject matter experts!

05:06

Answer 3

Textbook Question

Chapter 4, Problem 1E

Give the name of the algorithm that results from each of the following special cases:

a. Local beam search with k = 1.
b. Local beam search with one initial state and no limit on the number of states retained.
c. Simulated annealing with T = 0 at all times (and omitting the termination test).
d. Simulated annealing with T = ∞ at all times.
e. Genetic algorithm with population size N = 1.

Expert Solution & Answer

Explanation of Solution

a.

The local beam search with “k=1” is hill-climbing search.

b.

Local beam search with one initial state and no limit on the number of states retained resembles with Breadth-First search.
In breadth first search, before adding the next layer it adds one complete layer nodes.
Starting from one state, the algorithm would be essentially identical to breadth-ﬁrst search except that each layer is generated all at once.

c.

Simulated annealing with “T=0” at all time:

There is a fact that termination step would be triggered immediately. Ignoring this fact, the search would be identical to first choice hill climbing.
This is because; every downward successor would be rejected with probability 1.

d.

Simulated annealing with “T = ∞” at all times is a random-walk search, it always accepts a new state.

e.

Generic algorithm with population size “N=1”:

The two selected parents will be same individual, if the population size is “1”.
The crossover yields an exact copy of individuals. Here, the mutation chance occurs.
Thus, the algorithm executes a random walk in the space of individuals.

Want to see more full solutions like this?

Subscribe now to access step-by-step solutions to millions of textbook problems written by subject matter experts!

05:06

Answer 4

Textbook Question

Chapter 4, Problem 1E

Give the name of the algorithm that results from each of the following special cases:

a. Local beam search with k = 1.
b. Local beam search with one initial state and no limit on the number of states retained.
c. Simulated annealing with T = 0 at all times (and omitting the termination test).
d. Simulated annealing with T = ∞ at all times.
e. Genetic algorithm with population size N = 1.

Expert Solution & Answer

Explanation of Solution

a.

The local beam search with “k=1” is hill-climbing search.

b.

Local beam search with one initial state and no limit on the number of states retained resembles with Breadth-First search.
In breadth first search, before adding the next layer it adds one complete layer nodes.
Starting from one state, the algorithm would be essentially identical to breadth-ﬁrst search except that each layer is generated all at once.

c.

Simulated annealing with “T=0” at all time:

There is a fact that termination step would be triggered immediately. Ignoring this fact, the search would be identical to first choice hill climbing.
This is because; every downward successor would be rejected with probability 1.

d.

Simulated annealing with “T = ∞” at all times is a random-walk search, it always accepts a new state.

e.

Generic algorithm with population size “N=1”:

The two selected parents will be same individual, if the population size is “1”.
The crossover yields an exact copy of individuals. Here, the mutation chance occurs.
Thus, the algorithm executes a random walk in the space of individuals.

Want to see more full solutions like this?

Subscribe now to access step-by-step solutions to millions of textbook problems written by subject matter experts!

05:06

Answer 5

Textbook Question

Answer 6

Textbook Question

Answer 7

Expert Solution & Answer

Explanation of Solution

a.

The local beam search with “k=1” is hill-climbing search.

b.

Local beam search with one initial state and no limit on the number of states retained resembles with Breadth-First search.
In breadth first search, before adding the next layer it adds one complete layer nodes.
Starting from one state, the algorithm would be essentially identical to breadth-ﬁrst search except that each layer is generated all at once.

c.

Simulated annealing with “T=0” at all time:

There is a fact that termination step would be triggered immediately. Ignoring this fact, the search would be identical to first choice hill climbing.
This is because; every downward successor would be rejected with probability 1.

d.

Simulated annealing with “T = ∞” at all times is a random-walk search, it always accepts a new state.

e.

Generic algorithm with population size “N=1”:

The two selected parents will be same individual, if the population size is “1”.
The crossover yields an exact copy of individuals. Here, the mutation chance occurs.
Thus, the algorithm executes a random walk in the space of individuals.

Answer 8

Expert Solution & Answer

Explanation of Solution

a.

The local beam search with “k=1” is hill-climbing search.

b.

Local beam search with one initial state and no limit on the number of states retained resembles with Breadth-First search.
In breadth first search, before adding the next layer it adds one complete layer nodes.
Starting from one state, the algorithm would be essentially identical to breadth-ﬁrst search except that each layer is generated all at once.

c.

Simulated annealing with “T=0” at all time:

There is a fact that termination step would be triggered immediately. Ignoring this fact, the search would be identical to first choice hill climbing.
This is because; every downward successor would be rejected with probability 1.

d.

Simulated annealing with “T = ∞” at all times is a random-walk search, it always accepts a new state.

e.

Generic algorithm with population size “N=1”:

The two selected parents will be same individual, if the population size is “1”.
The crossover yields an exact copy of individuals. Here, the mutation chance occurs.
Thus, the algorithm executes a random walk in the space of individuals.

Answer 9

Expert Solution & Answer

Answer 10

Expert Solution & Answer

Answer 11

Explanation of Solution

a.

The local beam search with “k=1” is hill-climbing search.

b.

Local beam search with one initial state and no limit on the number of states retained resembles with Breadth-First search.
In breadth first search, before adding the next layer it adds one complete layer nodes.
Starting from one state, the algorithm would be essentially identical to breadth-ﬁrst search except that each layer is generated all at once.

c.

Simulated annealing with “T=0” at all time:

There is a fact that termination step would be triggered immediately. Ignoring this fact, the search would be identical to first choice hill climbing.
This is because; every downward successor would be rejected with probability 1.

d.

Simulated annealing with “T = ∞” at all times is a random-walk search, it always accepts a new state.

e.

Generic algorithm with population size “N=1”:

The two selected parents will be same individual, if the population size is “1”.
The crossover yields an exact copy of individuals. Here, the mutation chance occurs.
Thus, the algorithm executes a random walk in the space of individuals.

Answer 12

Ch. 4 - Give the name of the algorithm that results from...Ch. 4 - Prob. 2E Ch. 4 - Prob. 3E Ch. 4 - Prob. 5E Ch. 4 - Prob. 6E Ch. 4 - Prob. 7E Ch. 4 - Prob. 8E Ch. 4 - Prob. 9E Ch. 4 - Prob. 10E Ch. 4 - Prob. 12E

Videos

Explanation of Solution

Want to see more full solutions like this?

Chapter 4 Solutions

Additional Engineering Textbook Solutions