Optimization Description

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In a typical optimization problem, there are a number of variables which control the process, and a formula or algorithm which combines the variables to fully model the process. The problem is then to find the values of the variables that optimize the model in some way. If the model is a formula, then we will usually be seeking the maximum or minimum value of the formula.

For example, if your company has $100,000 to spend on different types of advertising such as TV, radio, and print, what is the best mix of the different types that will maximize sales? The dollars spent on each type of ad are the variables that control the process. A formula would be used to calculate sales dollars. The genetic algorithm would try different combinations of dollar values for each type of advertising until it found a combination that would maximize sales (as computed by the formula).

There are many mathematical methods which can optimize problems of this nature (and very quickly) for fairly "well behaved" problems. These traditional methods tend to break down when the problem is not so well behaved. Examples of these types of problems include combinatorial problems, or problems where the fitness function is not a smooth, continuous mathematical formula.