And we would be right. Genetic algorithms have been proven to be robust, flexible and efficient in vast complex spaces. So genetic algorithms provide robustness, efficiency and flexibility when searching a problem space for the optimum solution. For a more technical look at the power of GAs, a discussion on building blocks and schemata is required. For the moment, we shall just give a very brief look at the GAs search strategy.
GAs judiciously use the idea of randomness when performing a search.
However, it must be clearly understood that GAs are not simply random search algorithms, which will be dealt with later. Random search algorithms can be inherently inefficient due to the directionless neture of their search. GAs are not directionless. They utilize knowledge from previous generations of strings in order to construct a new generation that will approach the optimal solution. In other words, they use past knowledge to direct the search.