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Using the genetic algorithm Python code base provided as a starting point, implement a genetic algorithm to solve the knapsack problem. Please address the following by including the associated Python code excerpts (as appropriate) and explanation of the code in the PDF file: • createChromosome() logic • crossover () logic • Logic to compute chromosome fitness, e.g., any modifications to the evaluate() function • roulette Wheel() logic mutate() logic • insert() logic Apply the technique to the random problem instance and determine the best solution and objective value using your revised algorithm. Test multiple versions of the GA (e.g., different number of generations, population sizes, crossover rates, etc.) Method Table 1: Example of results summary (numbers are not realistic) SA to Cooling, tk Mk # of temps Iterations Items selected Weight Value to 1000 10 900 2102 87 320 3180 1+0.9k 800 0.99tk-1 50 40 5333 27 230 1284 1200 0.99tk-1 50 40 5333 13 1250 2002 Pop Items GA Generations Crossover Mutation Elitism Weight size selected tel:1284%2 1000 250 0.8 0.1 top 3 23 2650 1921 500 150 0.9 0.2 top 10% 39 1650 3914 etc.

Using the genetic algorithm Python code base provided as a starting point, implement a genetic algorithm to solve the knapsack problem. Please address the following by including the associated Python code excerpts (as appropriate) and explanation of the code in the PDF file: • createChromosome() logic • crossover () logic • Logic to compute chromosome fitness, e.g., any modifications to the evaluate() function • roulette Wheel() logic mutate() logic • insert() logic Apply the technique to the random problem instance and determine the best solution and objective value using your revised algorithm. Test multiple versions of the GA (e.g., different number of generations, population sizes, crossover rates, etc.) Method Table 1: Example of results summary (numbers are not realistic) SA to Cooling, tk Mk # of temps Iterations Items selected Weight Value to 1000 10 900 2102 87 320 3180 1+0.9k 800 0.99tk-1 50 40 5333 27 230 1284 1200 0.99tk-1 50 40 5333 13 1250 2002 Pop Items GA Generations Crossover Mutation Elitism Weight size selected tel:1284%2 1000 250 0.8 0.1 top 3 23 2650 1921 500 150 0.9 0.2 top 10% 39 1650 3914 etc.

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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Using the genetic algorithm Python code base provided as a starting point, implement a genetic algorithm to solve the knapsack problem. Please address the following by including the associated Python code excerpts (as appropriate) and explanation of the code in the PDF file: • createChromosome() logic • crossover () logic • Logic to compute chromosome fitness, e.g., any modifications to the evaluate() function • roulette Wheel() logic mutate() logic • insert() logic Apply the technique to the random problem instance and determine the best solution and objective value using your revised algorithm. Test multiple versions of the GA (e.g., different number of generations, population sizes, crossover rates, etc.)
Transcribed Image Text:Using the genetic algorithm Python code base provided as a starting point, implement a genetic algorithm to solve the knapsack problem. Please address the following by including the associated Python code excerpts (as appropriate) and explanation of the code in the PDF file: • createChromosome() logic • crossover () logic • Logic to compute chromosome fitness, e.g., any modifications to the evaluate() function • roulette Wheel() logic mutate() logic • insert() logic Apply the technique to the random problem instance and determine the best solution and objective value using your revised algorithm. Test multiple versions of the GA (e.g., different number of generations, population sizes, crossover rates, etc.)
Method Table 1: Example of results summary (numbers are not realistic) SA to Cooling, tk Mk # of temps Iterations Items selected Weight Value to 1000 10 900 2102 87 320 3180 1+0.9k 800 0.99tk-1 50 40 5333 27 230 1284 1200 0.99tk-1 50 40 5333 13 1250 2002 Pop Items GA Generations Crossover Mutation Elitism Weight size selected tel:1284%2 1000 250 0.8 0.1 top 3 23 2650 1921 500 150 0.9 0.2 top 10% 39 1650 3914 etc.
Transcribed Image Text:Method Table 1: Example of results summary (numbers are not realistic) SA to Cooling, tk Mk # of temps Iterations Items selected Weight Value to 1000 10 900 2102 87 320 3180 1+0.9k 800 0.99tk-1 50 40 5333 27 230 1284 1200 0.99tk-1 50 40 5333 13 1250 2002 Pop Items GA Generations Crossover Mutation Elitism Weight size selected tel:1284%2 1000 250 0.8 0.1 top 3 23 2650 1921 500 150 0.9 0.2 top 10% 39 1650 3914 etc.
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