11.(4 pts. Pseudo-code for the Simulated-Annealing algorithm is given below, note that in the version of the algorithm given, we wish to maximize the objective function (.c. walk uphill). function SIMULATED ANNEALING( problem, schedule) returns a solution state inputs, problem, a problem schedule a mapping from time to "temperature local variables: T. a "temperature controlling the probability of downward steps current ---MAKE-NoDeproblem INITIAL STATE) for t = 1 to do T-- schedule() if T=0 then return current next-- a randomly selected successor of current AE --next.VALUE - current.VALUE if AE >0 then current-nert else current - nert only with probability AE/T 6 Describe the idea behind the Simulated-Anncaling algorithm. Be sure to briefly explain the role of each component in the algorithm. Indicate how you could change the Simulated-Annealing algorithm so that it implements a "strict" version of hill-climbing Text ) With regards to Simulated-Anncaling, what is the probability of accepting the following moves? Assume the problem is trying to maximize the objective function (If you don't have a calculator, you can leave your answers in the form of mathematical expressions) Current Evil Tampet 30 15 13 DOLL 11.(4 pts. Pseudo-code for the Simulated-Annealing algorithm is given below, note that in the version of the algorithm given, we wish to maximize the objective function (.c. walk uphill). function SIMULATED ANNEALING( problem, schedule) returns a solution state inputs, problem, a problem schedule a mapping from time to "temperature local variables: T. a "temperature controlling the probability of downward steps current ---MAKE-NoDeproblem INITIAL STATE) for t = 1 to do T-- schedule() if T=0 then return current next-- a randomly selected successor of current AE --next.VALUE - current.VALUE if AE >0 then current-nert else current - nert only with probability AE/T 6 Describe the idea behind the Simulated-Anncaling algorithm. Be sure to briefly explain the role of each component in the algorithm. Indicate how you could change the Simulated-Annealing algorithm so that it implements a "strict" version of hill-climbing Text ) With regards to Simulated-Anncaling, what is the probability of accepting the following moves? Assume the problem is trying to maximize the objective function (If you don't have a calculator, you can leave your answers in the form of mathematical expressions) Current Evil Tampet 30 15 13 DOLL