The following list is a very quick look at the essential differences between GAs and other forms of optimization. For this case, strings representing the binary x values should be used. Genetic algorithms use a set, or population, of points to conduct a search, not just a single point on the problem space. This gives GAs the power to search noisy spaces littered with local optimum points. Instead of relying on a single point to search through the space, the GAs looks at many different areas of the problem space at once, and uses all of this information to guide it.
Genetic algorithms use only payoff information to guide themselves through the problem space.
Many search techniques need a variety of information to guide themselves. Hill climbing methods require derivatives, for example. GAs are probabiltistic in nature, not deterministic. This is a direct result of the randomization techniques used by GAs. GAs are inherently parallel.