Chapter Summary

    In this chapter, you learned about the simulated annealing algorithm. The simulated annealing algorithm is based on the actual process of annealing. The key point behind the annealing process is that a metal that is allowed to cool slowly will form more consistent, and therefore stronger, crystal structures. The reason being that the higher temperatures result in higher energy levels for the atoms that make up the metal. At the higher energy levels, the atoms have greater freedom of movement. As the metal cools, this freedom of movement is curtailed. This allows the atoms to settle into consistent crystal patterns.

    The process of simulated annealing is very similar to the actual annealing process. A series of input values are presented to the simulated annealing algorithm. The simulated annealing algorithm wants to optimize these input values so that an arbitrary equation can be minimized. Examples of equations to be minimized include the error function for a neural network, or the distance that a traveling salesman travels. The input values, which drive the simulated annealing algorithm, can be the weight matrix of a neural network or the current route between cities that a traveling salesman is traveling.

    To present a relatively simple example of how to use simulated annealing, this chapter once again turned to the traveling salesman problem. The traveling salesman problem was also used in chapter 6 in conjunction with genetic algorithms. Reusing the traveling salesman problem allows us to easily compare the performance of genetic algorithms with simulated annealing.