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t for several problems you will modify some provided Python code to implement algorithms to solve the same instance of the knapsack problem. After implementing all of the code and solving the problem, you must provide a single table of all results similar to the following: Table 1: Example of results summary (numbers are not realistic) Algorithm Local Search (Best Improvement) 200 Local Search with Random Restarts (k = 100) Local Search with Random walk (p=0.25) etc. Iterations #Items Selected Weight Objective 3102 9510 2102 49 97 117 121 21 147 87 32 184 Knapsack Problem Definition Given n different items, where each item i has an assigned value (v.) and weight (w), select a combination of the items to maximize the total value without exceeding the weight limitations, W, of the knapsack.

t for several problems you will modify some provided Python code to implement algorithms to solve the same instance of the knapsack problem. After implementing all of the code and solving the problem, you must provide a single table of all results similar to the following: Table 1: Example of results summary (numbers are not realistic) Algorithm Local Search (Best Improvement) 200 Local Search with Random Restarts (k = 100) Local Search with Random walk (p=0.25) etc. Iterations #Items Selected Weight Objective 3102 9510 2102 49 97 117 121 21 147 87 32 184 Knapsack Problem Definition Given n different items, where each item i has an assigned value (v.) and weight (w), select a combination of the items to maximize the total value without exceeding the weight limitations, W, of the knapsack.

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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t for several problems you will modify some provided Python code to implement algorithms to solve the same instance of the knapsack problem. After implementing all of the code and solving the problem, you must provide a single table of all results similar to the following: Table 1: Example of results summary (numbers are not realistic) Algorithm Iterations Items Selected Weight Objective Local Search (Best Improvement) 3102 49 97 117 Local Search with Random Restarts (k = 100) Local Search with Random walk (p = 0.25) 9510 121 21 147 2102 87 32 184 etc. Knapsack Problem Definition Given n different items, where each item i has an assigned value (v.) and weight (w), select a combination of the items to maximize the total value without exceeding the weight limitations, W, of the knapsack. IMPORTANT!: When generating random problem instance set you must use the code provided and values: n = 150; max weight of 2500; and, use a seed value (for the random number generator) of 51132023.
Transcribed Image Text:t for several problems you will modify some provided Python code to implement algorithms to solve the same instance of the knapsack problem. After implementing all of the code and solving the problem, you must provide a single table of all results similar to the following: Table 1: Example of results summary (numbers are not realistic) Algorithm Iterations Items Selected Weight Objective Local Search (Best Improvement) 3102 49 97 117 Local Search with Random Restarts (k = 100) Local Search with Random walk (p = 0.25) 9510 121 21 147 2102 87 32 184 etc. Knapsack Problem Definition Given n different items, where each item i has an assigned value (v.) and weight (w), select a combination of the items to maximize the total value without exceeding the weight limitations, W, of the knapsack. IMPORTANT!: When generating random problem instance set you must use the code provided and values: n = 150; max weight of 2500; and, use a seed value (for the random number generator) of 51132023.
Modify the completed Python Local Search code to implement Hill Climbing with Random Restarts. You may use Best Improvement or First Improvement (just clearly state your choice). Make sure to include the following: Make the number of random restarts, k, an easily modifiable parameter. Keep track of the best solution found across all of the restarts. Apply the technique to the random problem instance and determine the best solution and objective value using your algorithm.
Transcribed Image Text:Modify the completed Python Local Search code to implement Hill Climbing with Random Restarts. You may use Best Improvement or First Improvement (just clearly state your choice). Make sure to include the following: Make the number of random restarts, k, an easily modifiable parameter. Keep track of the best solution found across all of the restarts. Apply the technique to the random problem instance and determine the best solution and objective value using your algorithm.
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