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This is a map of 7 cities (A, B, C, D, E, F, and G) which are connected with each other via different paths. Your job is to visit every city just once covering the minimum distance possible. You have to find the optimal combination of cities using Genetic Algorithm. You can start at any point and end at any point but keep in mind that every city must be visited. 1. Encode the problem and create an initial population of 4 different chromosomes. [' 2. Using an appropriate fitness function, perform natural selection to choose the 2 fittest chromosomes. [ 3. Perform single point crossover from the 3rd index of your selected chromosomes to get 2 offspring. Are they eligible as a solution? If not, explain with reason. "i

This is a map of 7 cities (A, B, C, D, E, F, and G) which are connected with each other via different paths. Your job is to visit every city just once covering the minimum distance possible. You have to find the optimal combination of cities using Genetic Algorithm. You can start at any point and end at any point but keep in mind that every city must be visited. 1. Encode the problem and create an initial population of 4 different chromosomes. [' 2. Using an appropriate fitness function, perform natural selection to choose the 2 fittest chromosomes. [ 3. Perform single point crossover from the 3rd index of your selected chromosomes to get 2 offspring. Are they eligible as a solution? If not, explain with reason. "i

Computer Networking: A Top-Down Approach (7th Edition)
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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15 10 8 14 7 3 8 2 11 17 This is a map of 7 cities (A, B, C, D, E, F, and G) which are connected with each other via different paths. Your job is to visit every city just once covering the minimum distance possible. You have to find the optimal combination of cities using Genetic Algorithm. You can start at any point and end at any point but keep in mind that every city must be visited. 1. Encode the problem and create an initial population of 4 different chromosomes. [ ' 2. Using an appropriate fitness function, perform natural selection to choose the 2 fittest chromosomes. 3. Perform single point crossover from the 3rd index of your selected chromosomes to get 2 offspring. Are they eligible as a solution? If not, explain with reason. "i 4. Would the usual method of mutation work here? Explain your opinion. :
Transcribed Image Text:15 10 8 14 7 3 8 2 11 17 This is a map of 7 cities (A, B, C, D, E, F, and G) which are connected with each other via different paths. Your job is to visit every city just once covering the minimum distance possible. You have to find the optimal combination of cities using Genetic Algorithm. You can start at any point and end at any point but keep in mind that every city must be visited. 1. Encode the problem and create an initial population of 4 different chromosomes. [ ' 2. Using an appropriate fitness function, perform natural selection to choose the 2 fittest chromosomes. 3. Perform single point crossover from the 3rd index of your selected chromosomes to get 2 offspring. Are they eligible as a solution? If not, explain with reason. "i 4. Would the usual method of mutation work here? Explain your opinion. :
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