Based on the material we covered in the previous sections, genetic algorithms are best suited for the following types of problems:
- Problems with complex mathematical representation: Since genetic algorithms only require the outcome of the fitness function, they can be used for problems with target functions that are hard or impossible to differentiate, problems with a large number of parameters, and problems with a mix of parameter types.
- Problems with no mathematical representation: Genetic algorithms don't require a mathematical representation of the problem as long as a score value can be obtained or a method is available to compare two solutions.
- Problems involving a noisy environment: Genetic algorithms are resilient to problems where data may not be consistent, such as data originating from sensor output or from human-based scoring.
- Problems involving an environment that changes over time: Genetic algorithms can respond to slow changes in the environment by continuously creating new generations that will adapt to the changes that occur.
On the other hand, when a problem has a known and specialized way of being solved, using an existing traditional or analytic method is likely to be a more efficient choice.
