this question is from my distributed system assignment $,$please give me the matlab coding from this algo, l want to do similation and check the performance $...$GA method

The optimization approach known as GA is frequently employed in complicated and massive systems to deter$-$

mine results near the optimal level. Consequently, GA is an excellent technique for training a neural network

model for learning. A standard GA is based on a population search method influenced by the process of natural

selection that relies on the concept of persistence of the healthiest ${?}^{40}.$ GA's primary components are $($a$)$ chromo$-$

some, $($b$)$ selection process, $($c$)$ mutation process, $($d$)$ crossover, and $($e$)$ calculation and evaluation of fitness

function.

We start by arbitrarily initializing a population of chromosomes, which we typically consider as potential

alternatives to scheduling for any specific task. The allocation of activities to certain machines inside that chro$-$

mosome allows us to obtain a fitness value $($Makespan$),$ which is acquired. After receiving the initial population,

we assess each chromosome in the group according to its unique fitness value.

A smaller makespan is always desired to fine$-$tune the mapping. We use an allocation scheme that statistically

replicates a specific chromosome and eliminates others. At the same time, we discover that improved mappings

are more likely to be repeated in future generations. At the same time, the number of individuals stays constant

over each age. Algorithm $1$ presents the working of the GA method ${?}^{41}.$

Input: Population set $($Ps$),$ Probability of Crossover $($Cp$),$ Probability of Mutation $($Cm$),$

Output: Best chromosomes

The initialization of variables, i$.$e$.,$ Population, Population dimension vector $(P_{VD})$

$P_s=[P_1$dotsdots.$P_n]$ and $P_{VD}=[P_{i1,\text{d}\text{o}\text{t}\text{s}\text{d}\text{o}\text{t}\text{s}\text{d}\text{o}\text{t}\text{s}}dots{P}_{VD}],$

Initial Gen $=1,$ Gen ${?}_{\text{m}\text{a}\text{x}}$: Maximum generation

While$($Gen $$ Gen ${?}_{\text{m}\text{a}\text{x}})//$Repeat steps $3$ to

Determine the fitness value by fitness function eq$.(1)$

$3\mathrm{.}1$ Fitness $=\frac{1}{\text{M}\text{e}\text{a}\text{n}\text{A}\text{b}\text{s}\text{o}\text{l}\text{u}\text{t}\text{e}\text{E}\text{r}\text{r}\text{o}\text{r}}$

$3\mathrm{.}2$ A Mean Absolute Error $($MAE$)$ can be calculated using equation $2.$

MAE$=\frac{1}{N}{\displaystyle \underset{i=0}{\overset{N}{}}}[|y_i-z_i|]$

Where MAE is Mean Absolute Error, $y_i$ is the predicted and $z_i$ is the actual, and $N$ is

the number of variables.

The average of MAE can be measured by eq $(3).$

MAE$=\frac{1}{N}{\displaystyle \underset{i=0}{\overset{N}{}}}[|e_i|]$

Where $|e_i|$ : Input count can be calculated using equation $3\mathrm{.}1.$

$|e_i|=|y_i-z_i|$

Determine best chromosome $//$ call$\_$Det$\_$best$\_$chro$()$

Select best chromosome $//$ call$\_$Sel$\_$best$\_$chro$()$

Determine Crossover $//$ Crossover$()$

Determine mutation $//$ mutation$()$

Return Best chromosomes

END

Algorithm $1$ GA algorithm