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# Optimization

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Optimization, covered in Chapter 5, is a little different from the other methods; instead of working with a dataset, it attempts to select values that minimize the output of a cost function. Chapter 5 showed several examples of cost functions, such as planning group travel using a combination of price and waiting time at the airport, assigning students to the most appropriate dorm, and optimizing the layout of a simple graph. Once the cost function was designed, the same algorithms could be used to solve these three different problems. Two algorithms were covered: simulated annealing and genetic algorithms.

A cost function is any function that takes a guess at a solution and returns a value that is higher for worse solutions and lower for better solutions. Optimization algorithms use this function to test solutions and to search possible solutions for the best one. The cost functions you use with optimization often have many variables to consider, and it’s not always clear which is the best one to change in order to improve the result. However, for illustration, consider a function with only one variable, defined as: