GAs can be computationally expensive, especially for large-scale problems. This is because they require a large number of fitness evaluations, which can be computationally intensive. This makes GAs less suitable for problems where computational resources are limited.
Another significant limitation is premature convergence. This is when the algorithm converges on a sub-optimal solution too early, before it has had a chance to explore other potentially better solutions. This can happen if the population of solutions becomes too similar to each other, which reduces the diversity of the population and limits the search space. This is a particular problem in GAs because they use a fitness-based selection process, which can lead to the over-representation of certain solutions in the population
GAs also require careful parameter tuning. The performance of a GA can be greatly affected by the choice of parameters, such as the population size, mutation rate, and crossover rate. However, there is no one-size-fits-all set of parameters that works best for all problems. Therefore, finding the right parameters can be a time-consuming process that requires a lot of trial and error.