Algorithm 3: The Real-Coded GA

1:    Create N random points in $\mathsf{\Omega }$ from the uniform distribution.

2:    Store the points in set S.

3:    $iter=0$.

4:    Evaluate each chromosome using its function value.

5:    If the termination criteria are achieved, stop the GA.

6:    Select $m\le N$ parents from S.

7:    Create m offspring using the selected parent chromosomes of the previous step.

8:    Mutate the offspring with probability $p_m$.

9:    Remove the m worst chromosomes and replace them with the offspring.

10: Create a trial point $\tilde{x}$. If $f\left(\tilde{x}\right)\le f\left(x_h\right)$ where $x_h$ is the current worst point in S, then replace $x_h$ with $\tilde{x}$.

11: $iter=iter+1$.

12: Go to step 4.