This problem arises for many reasons. Understanding why certain problems are GA-hard is a very important research problem in the GA community. According to eq 7, schemata of longer length have a higher probability of being destroyed by mutation and recombination. As discussed in reference 51, GA-hardness can also occur when the genes are highly correlated with each other, thus causing the order of the building blocks to be too high. Even for a GA-hard optimization problem, however, the GA may be able to find the global optimum by employing high mutation rates and becoming a more stochastic technique or through other genetic operators.
GAs have several advantages when compared to other optimization algorithms.
They have the ability to move from local optima present on the response surface. For applications where the building block principle is followed, GAs use implicit parallelism to process information quickly and require fewer response function evaluations that other automated numerical optimization algorithms. GAs optimize on the representation of the variables and not the variables themselves, hence making them very flexible. One of the drawbacks to GAs is that there is no a priori method for determining whether a particular application is GA-hard. The major drawback to GAs, however, is the configuration of the algorithm.