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."