Task $1)$ Fitness Functions Use MATLAB script to program the following fitness functions. These functions are for minimisation problems and will be used to validate the performance of optimisation algorithms. For all the above functions, set D$=10.$ Task $2)$ Design a Genetic Algorithm Write a Matlab code for a Genetic Algorithm. The following operators should be used Selection operator: Tournament selection Crossover operator: Whole arithmetic crossover Mutation operator: Uniform mutation Other parameter settings: Set total number of generations Tmax$=100.$ Set population size N$=50.$ Set offspring population size M$=100.$ Set the intermediate population in the mating pool $100.$ Set the total number of iteration $1000.$ Test the Genetic Algorithm against the four fitness functions in Task $1.$ Repeat every optimisation process for $20$ times and show the average performance $($fitness value$).$ Change the selection operator to Proportional Selection and re$-$run all the previous optimisation experiments. Compare the result with the initial one and comment on it$.$ Change the crossover operator to Simple$-$point arithmetic crossover and re$-$run all the optimisation experiments. Compare the result with the initial one and comment on it$.$ Please answer both tasks fully with every part of the matlab script included.