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And if it is not true, what are the problem classes or characteristics for which genetic algorithms are superior?

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What you might have heard could have been a formulation of a No Free Lunch Theorem. Loosely, those results say that you can either have an optimization algorithm that does fairly well on many problems, or really well on some problems, but not both.

Genetic Algorithms are rather broad classes of optimizers, so you can typically apply them to many types of problems with decent results, but for specific problems there will typically be more efficient algorithms that only work for this type of problem.

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