Q4: The knapsack problem can be defined as follows: Given a sel of items, each with a weight and a value, determine the items to be included in the knapsack so that the total weight is less than or equal to a given limit and the total value is as large as possible. If you know that lie hnapsack holds a maximum of 22 and you have the following items: Determine the next population that results after one iteration of Genefic Agorithms. Assume that: 1- Population size (N)=6. 2- Roulette wheel selection. 3- Single point crossover with Pc=0.7 and the crossover point is is that between the 3rd and the fth positions. 4- Flip mutation with Pm=0.1 and the mutation point is in the first bit (from left). 5- Replacement strategy: the best individuals in the parent and offspring populations are selected without repetition. If you need random numbers, choose in order from the following: 0.86,0.59,0.67,0.14,0.34,0.08, 0.11,0.29,0.85,0.76,0.43,0.47,0.89,0.80,0.98,0.58,0.03,0.57,0.49,0.92. Repeat from becimming if Q3: Use genetic algorithms to solve the job scheduling problem in grid computing, which can be defined as follows: 1. A set of n jobs that have to be scheduled. These jobs are independent to each other (i.e., any job can be processed by any resource) and are non-preemptive, which means that a job must be processed entirely by a single resource. 2. A set of m resources to process the submitted set of independent jobs. These resources are heterogeneous. 3. The ETC matrix of size nm, where ETClilij represents the estimated time for executing job i on resource j. The goal of job scheduling in grid computing is to find a mapping of the submitted jobs to the available resources that minimizes the makespan, which itself represents the finishing time of the latest task and can be computed by: : makespan =minxsmaxjes (Finish j) where S is the set of all possible solutions, J is the set of all jobs submitted to the system and Finish j represents the time when job j is finished