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5. Optimization > Hill Climbing

Hill Climbing

Randomly trying different solutions is very inefficient because it does not take advantage of the good solutions that have already been discovered. In our example, a schedule with a low overall cost is probably similar to other schedules that have a low cost. Because random optimization jumps around, it won’t automatically look at similar schedules to locate the good ones that have already been found.

An alternate method of random searching is called hill climbing. Hill climbing starts with a random solution and looks at the set of neighboring solutions for those that are better (have a lower cost function). This is analogous to going down a hill, as shown in Figure 5-1.


  

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