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Method: Genetic Algorithm Objective Function: f ( x 1 , x 2 , dots, x n ) = ? ? n 2 i = 1

Method: Genetic Algorithm
Objective Function: f(x1,x2,dots,xn)=??n2i=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."
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