Answer to the below question for the following scenario, with relevant justification. Definition will not be accepted. Vague answered will be penalized. Assumptions if anyshould be stated at the beginning of the answer.

a. Problem: "In Machine Learning modelling in addition to dataset inputs, some 202 Proble algorithms may need setting up a few hyper parameters whose value is used to control the learning/training process. Hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. You are asked to design the given problem using genetic algorithm. Use any machine learning model of your choice which has atleast 2 hyperparameters and

Explain with short answers, your design approach in terms of following:

i. The Chromose/String representation of a state constituting a parent

ii. Design of fitness function,

iii. Suggest an appropriate process of selection & crossover alone with numerical example.

b. "Informed search is preferred than Local search algorithms". Justify this statement with appropriate plagiarism free scenario or numerical example. c. "Randomization in the local search algorithm benefits the optimization problem' Justify this statement with appropriate plagiarism free numerical example.