Saga Quiz
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SA Questions
 SA always accepts transitions that improve the solution.
 True
 False
 The likelihood with which SA accepts solutionworsening transitions depends on the temperature, the magnitude of the change in energy, and the solution where it currently is at.
 True
 False
 Steepest descent behaves no differently than simulated annealing at zero temperature.
 True
 False
 SA does not give the same results in Miami as in Anchorage because of the difference in temperature between the two cities.
 True
 False
 SA accepts more transitions at higher temperatures.
 True
 False
 Solutions from SA can be worse than those from steepest descent.
 True
 False
 SA has to follow a slow coolingoff schedule to ensure the quality of the solution.
 True
 False
 Initial conditions are immaterial for SA. Indeed, SA will come by great solutions irrespective of the starting point.
 True
 False
 One needs to jump in and stop SA after a certain number of iterations. Otherwise, SA may stray away from the optimal solution.
 True
 False
 SA may not ascertain it found the optimal solution.
 True
 False
 Endowing SA with memory (say, by keeping track of the top X solutions encountered) amounts to exploring the solution space more efficiently.
 True
 False
 In SA, there are two probabilities: one is involved in defining the candidate solution to be evaluated (left or right transitions in the Bayser SA tool), and the other in establishing the acceptance threshold against which the candidate solution is measured. Both probabilities are related to the temperature.
 True
 False
 What helps SA get out of local minima?
 The acceptance threshold is established probabilistically.
 The exponential form of the Metropolis condition, i.e., that p is less than exp (ΔE/kT) where ΔE is the change in energy, T the temperature, and k is a constant.
 Annealing follows a declining temperature schedule.
 Positive energy changes are not discarded automatically.
 What contributes to the quality of the solutions in SA?
 Temperature schedule.
 Randomness of the search.
 Initial conditions.
 All of the above.
 How does the Objective Function impact the SA process?
 SA breaks down for very discontinuous objective functions.
 SA can only work if the objective function admits multiple minima.
 SA requires that the Objective function be convex.
 SA only works for continuous Objective Functions.
 Dartthrowing consists of randomly picking a candidate from the solution space. That candidate is accepted if it is the best solution found. In the light of this definition, what is the most fundamental difference between SA and dartthrowing?
 SA uses two probabilities while dartthrowing uses just one.
 SA uses a temperature, dartthrowing does not.
 The acceptance criterion for SA is probabilistic, that of dartthrowing deterministic.
 SA explores the solution space in the vicinity of the current solution, dartthrowing can go anywhere.
 SA needs to remember the best solutions it comes across if a rising temperature schedule is followed. This is because:
 The probability of accepting any transition is increasing.
 SA may get further from the optimal solution as the process unfolds.
 SA will behave more and more like dartthrowing.
 All of the above.
 Can dart throwing somehow be used to improve SA?
 Dart throwing and SA are two different techniques and have nothing to do with each other.
 SA has a small bandwidth and dartthrowing a large one. Just like we use the viewer of a telescope to zero in on the celestial object of interest before observing the object in the eyepiece, we can first use dartthrowing to identify a promising region of the solution space, then use SA to zoom in that region.
 Dart throwing provides better initial solutions than SA. Therefore, SA can start off with solutions generated by dart throwing, hence the gain in efficiency.
 Alternate between dartthrowing and SA to improve "finding power". Indeed, dartthrowing will allow SA to get out of deep local minima.
 Territory alignment consists of carving out geographies for members of the sales organization. One of the key objectives is that the workload be balanced. How would you deploy SA to automate territory alignment?
 Parallel processing is a very powerful technique to cut down on execution time. How would you modify the SA framework to take advantage of parallel processing?
 The fact that the left and right transitions in the Bayser SA tool are generated with the same probability, namely 50%, suggests that, all things being equal, the algorithm will end up where it started. What if some asymmetry were introduced, for instance 40% for the left transition and 60% for the right transition? What about running two copies of SA, one with 6040 and the other with 4060?
 Endowing memory to SA may be taken to mean keeping track of the top X solutions. What are the implications of such an addition on the performance of SA?
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