For the six cases listed in the following table, calculate the probability of the newly generated candidate neighbour point being accepted by the simulated annealing algorithm (assume the energy function is being minimized here). Give your answers to 3 decimal places and clearly show all workings (including any formulas used). (6 marks) Suppose you are using simulated annealing to minimize the energy function of a problem and you use a temperature cooling schedule that lowers the temperature parameter very quickly. Is it then likely that simulated annealing will find the globally optimal solution? Explain why or why not. (4 marks) You wish to use the Simulated Annealing approach on an algorithm to find a point on a map of a desired height. You have two control variables x and y that determine your position on the map. You also have a height(x,y) that will return the height on the map of the given control variables. Write an energy 0 function that compares the height of the current point against the required height (required is the name of variable for this). Also write a move0 function that will randomly pick a control variable and will randomly offset that variable in either direction by 0.1. It should be equally likely that all combinations of control variable and direction is chosen. Provide python code for both methods