Method: Genetic Algorithm Objective Function: f(x1, x2, ...,xn) = xi n 2 i-1 (minimization) ...

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■Method: Genetic Algorithm ■ Objective Function: f(x1, x2, ...,xn) = xi n 2 i-1 (minimization) ■ Maximum Number of Objective Function Evaluations: 10,000 ■ Population Size: 20 ■ Problem Dimension: 5 ■ Each Decision Variable will be represented by 15 bits. ▪ Length of each individual in the population: 75 bits ■ Decision Variables Bounds: Real numbers in the range [-100, 100] ■ Matching: Roulette Wheel Selection (as done in Example-1 in the lecture) ■ Crossover Probability: 0.7 ■ Crossover: Two-point Crossover ■ Mutation Probability: 0.001 ■ Mutation: Bit Inversion ■ The decision variables and the objective function value of the best individual reached at the end of each iteration will be displayed. "Please code the Genetic Algorithm in the C programming language with these parameters and provide explanations for each step." ■Method: Genetic Algorithm ■ Objective Function: f(x1, x2, ...,xn) = xi n 2 i-1 (minimization) ■ Maximum Number of Objective Function Evaluations: 10,000 ■ Population Size: 20 ■ Problem Dimension: 5 ■ Each Decision Variable will be represented by 15 bits. ▪ Length of each individual in the population: 75 bits ■ Decision Variables Bounds: Real numbers in the range [-100, 100] ■ Matching: Roulette Wheel Selection (as done in Example-1 in the lecture) ■ Crossover Probability: 0.7 ■ Crossover: Two-point Crossover ■ Mutation Probability: 0.001 ■ Mutation: Bit Inversion ■ The decision variables and the objective function value of the best individual reached at the end of each iteration will be displayed. "Please code the Genetic Algorithm in the C programming language with these parameters and provide explanations for each step."

Answer rating: 100% (QA)

Heres a breakdown of the parameters and explanations for each step Method Genetic Algorithm GA Objective Function This function determines a solutions fitness You want to minimize the function given b... View the full answer