Order the answer to: Implement a simple GA with fitness-proportionate selection, roulette-wheel sampling, population…

Order the answer to: Implement a simple GA with…

Question	TASK a) Implement a simple GA with fitness-proportionate selection, roulette-wheel sampling, population size100, single-point crossover rate pc = 0.7, and bitwise mutation rate pm = 0.001. The used chromosome representation is the 64-bit IEEE-754 double precision format. b) The fitness function is designed to estimate the root of the function: ??(??) = ?? 10 ?17 – 200 ?? ? ?? ? ? + 21 Consider the search range is within [-100, 100]. 2. Result Analysis TASK a) Report results of five runs. Every result set is obtained by running the GA for 100 generations and plotting the fitness of the best individual found at each generation as well as the average fitness of the population at each generation. b) Discuss the convergence consistency of GA to a correct root.
Subject	python programming

Question

TASK a) Implement a simple GA with fitness-proportionate selection, roulette-wheel sampling, population size100, single-point crossover rate pc = 0.7, and bitwise mutation rate pm = 0.001. The used chromosome representation is the 64-bit IEEE-754 double precision format. b) The fitness function is designed to estimate the root of the function: ??(??) = ?? 10 ?17 – 200 ?? ? ?? ? ? + 21 Consider the search range is within [-100, 100]. 2. Result Analysis TASK a) Report results of five runs. Every result set is obtained by running the GA for 100 generations and plotting the fitness of the best individual found at each generation as well as the average fitness of the population at each generation. b) Discuss the convergence consistency of GA to a correct root.

Subject

python programming

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