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Artificial Intelligence Tutorials: Data Mining ▫ Neural Networks ▫ Fuzzy Logic ▫ Genetic Algorithms  Tutoriales de Inteligencia artificial: Redes Neuronales ▫ Lógica Fuzzy ▫ Minería Datos ▫ Algoritmos Genéticos  
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Limitations and Possibilities
The
representation of the problem may be difficult. You need to identify which
variables are suitable to be treated as genes and which variables must be
let outside the GA process. 

As in any
optimization process, if you don’t take enough precautions, the algorithm
may converge in a local minimum (or maximum). For all the above reasons, if you know a traditional method to solve the problem, use it! Try GA's if the system is partially unknown, nonlineal, or noisy. Even in this case, be sure that you are able to program the fitness evaluation function. In many cases this is by far the hardest part of the work. On the other hand, these concepts have been very useful to provide solutions where traditional methods failed. As a parallel computation process, the GA may explore the solution space in many directions and from many points.  
Complex
environments with nonlineal behavior are good candidates to be worked with GA's. The fitness function may be discontinuous and even changing over time. 





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