Advantages of Genetic Algorithms

• Can handle large search spaces
• Can search for the global optimum
• Can work with incomplete or noisy data
• Can be used in situations where the objective function is non-linear or non-differentiable

Limitations of Genetic Algorithms

• Can become trapped in local optima
• Can be computationally expensive
• Requires a good understanding of the problem and the selection of appropriate parameters

Despite these limitations, genetic algorithms have been successfully applied to a wide range of optimization problems, including engineering design, scheduling, and finance.

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