Question

1. 3pts: The Simulated Annealing algorithm can be used to mitigate against the possibility of a search getting stuck in, for example, a local minimum.

what is another common technique that addresses that same end goal? please explain what it is and how they are similiar.

Answer 1

Another common technique that addresses that same end goal is Hill climbing.

Hill Climbing	Simulated Annealing
In Hill Climbing we start with a solution to an optimization problem, often a random solution.Then we will make a modification to that solution (a local move) and check if the new solution is better or worst than the previous one. if it worst you keep the original solution and suggest a new change.	Simulated annealing is modeled after a physical process when has a better chance of finding a near optimal global solution
Hill climbing only updates when it founds a better solution.	Simulated Annealing has a mechanism to escape from local optimum accepting worst solutions with a given probability.
Hill Climbing is excellent when there is only a global maximum/minimum or when the local optimum is quite similar to the local optima	Simulated annealing is a good meta-heuristic because you can quickly find good solutions to very difficult problems without even thinking about them, it can be used in the Traveling Salesman problem, decrypting cyphered messages and many other problems