Consider the problem of finding the shortest route through several cities, such that each city is visitedonly once and in the end return to the starting city (the Travelling Salesman problem). Suppose thatthere 8 cites and there is a direct route between each two cities, the position of cities in the map areas fallow.CityCityt#1City#2City#3City#4City#5City#6City#7City#8Y1.85.68.58.13.212.61.910.910.82.911.64.89.74.24.17.6Based on this position the distance between each two city can simplify calculated based on Euclideandistance. For example the distance of Cityt#1 and City#3 is:Distance(City#1, City #3) = (Xcity41-Xcuyws)+ (Yetywi-Ycuyw3)*= /(1.8 - 3.2) + (5.6 - 12.6)2 = 7.138Use genetic algorithm in order to solve this problem, in which genes represent route between pairs ofcities (not the city index). So you have labeled each route and use that label as genes value, as there is8 city and all of cities connected to each other there is 28 route and so lower bound of genes valuebecome 1 and the upper bound become 28.Implement the solution for this problem, and describe the fallowing items:a) Is this a permutation problem? If so, which type of crossover and mutation operator you usedb) How you define the fitness function, is there any penalty in fitness function definition.c) If an unfeasible chromosome generated (a chromosome which represent a traveling path inwhich it cannot be the problem solution), how you handle that chromosomes.Submit:• Implemented code• The plot of genetic algorithm run (average and best fitness value over generations)• Best achieved result (the path and overall distance)• The description of mentioned items

Mobile Salesman Problem (TSP): Given a bunch of urban communities and distance between each pair of…

Consider the problem of finding the shortest route through several cities, such that each city is visited only once and in the end return to the starting city (the Travelling Salesman problem). Suppose tha there 8 cites and there is a direct route between each two cities, the position of cities in the map are as fallow.

Consider the problem of finding the shortest route through several cities, such that each city is visited only once and in the end return to the starting city (the Travelling Salesman problem). Suppose tha there 8 cites and there is a direct route between each two cities, the position of cities in the map are as fallow.

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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Question

Consider the problem of finding the shortest route through several cities, such that each city is visited
only once and in the end return to the starting city (the Travelling Salesman problem). Suppose that
there 8 cites and there is a direct route between each two cities, the position of cities in the map are
as fallow.
City
Cityt#1
City#2
City#3
City#4
City#5
City#6
City#7
City#8
Y
1.8
5.6
8.5
8.1
3.2
12.6
1.9
10.9
10.8
2.9
11.6
4.8
9.7
4.2
4.1
7.6
Based on this position the distance between each two city can simplify calculated based on Euclidean
distance. For example the distance of Cityt#1 and City#3 is:
Distance(City#1, City #3) = (Xcity41-Xcuyws)+ (Yetywi-Ycuyw3)*
= /(1.8 - 3.2) + (5.6 - 12.6)2 = 7.138
Use genetic algorithm in order to solve this problem, in which genes represent route between pairs of
cities (not the city index). So you have labeled each route and use that label as genes value, as there is
8 city and all of cities connected to each other there is 28 route and so lower bound of genes value
become 1 and the upper bound become 28.
Implement the solution for this problem, and describe the fallowing items:
a) Is this a permutation problem? If so, which type of crossover and mutation operator you used
b) How you define the fitness function, is there any penalty in fitness function definition.
c) If an unfeasible chromosome generated (a chromosome which represent a traveling path in
which it cannot be the problem solution), how you handle that chromosomes.
Submit:
• Implemented code
• The plot of genetic algorithm run (average and best fitness value over generations)
• Best achieved result (the path and overall distance)
• The description of mentioned items

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