Method: Genetic Algorithm

Objective Function: $f(x1,x2,$dots,$xn)={\displaystyle \underset{?}{\overset{?}{}}}n2i=1($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: $0\mathrm{.}7$

Crossover: Two$-$point Crossover

Mutation Probability: $0\mathrm{.}001$

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."