Genetic Algorithms are heuristic search routines that are guided by a model of Darwin's theory of natural selection or the survival of the fittest. The basic idea behind the genetic search strategy is to generate solutions that converge on the global maximum (i.e., the best solution in the search space) regardless of the "terrain" of the search space. The basic operations involved in a genetic algorithm are: 1) mate selection, 2) crossover, and 3) mutation. Typically, the major data structure is a binary string representing the possible solutions. Simulated Annealing on the other hand is a heuristic search technique based on a model of the annealing process in metalwork. More specifically, the analogy is with thermodynamics and how metals cool and anneal. Slow cooling causes the atoms to reach a low energy state (all lined up so to speak). This results in a less brittle final product; an important feature to folks going off to fight in the Crusades back in the 1100's.