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Genetic algorithms (GAs) seek to solve optimization problems using the methods of evolution, specifically survival of the fittest. 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 which 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. 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. 