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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.

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.

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.
During evaluation of a candidate solution, it may be discovered to be infeasible. In this case, provide two strategies for handling in feasibility. Copy and paste relevant excerpts of commented Python code for the handling infeasibility.
Transcribed Image Text:During evaluation of a candidate solution, it may be discovered to be infeasible. In this case, provide two strategies for handling in feasibility. Copy and paste relevant excerpts of commented Python code for the handling infeasibility.
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