Summary

In this chapter, we've looked at the simulated annealing algorithm as a way to perform search and optimization procedures. Simulated annealing follows a physical metaphor of a solid heated to its melting point and then cooled slowly to form a uniform solid. At high temperatures , the search process is permitted to search over the entire landscape. As the temperature is reduced, the search space decreases to the local area around the current solution. To illustrate the algorithm, we solved the classic N-Queens problem up to N=50. Finally, we investigated the parameters of simulated annealing and how they could be manipulated to help solve more complex problems, or simpler problems with greater speed.