With the population initialization complete, a GA can start to work on its principal job: evolving the population. Evolution of the population normally has a very precise goal: production of the best solution possible in the time given. To attain this goal, the genetic algorithm uses three main operations. These are, the selection of a parent or parents to undergo some genetic operation, the production of offspring by mutating and/or combining the parent(s) and merging the offspring and current population into a new one. Each "round" of these operations is termed a generation. Generations are repeated as long as is deemed necessary (usually up to a certain time limit) with the goal of improving the average quality of the solutions in the population. (Improving average quality normally leads to an improvement of the highest quality solution in the population--the ultimate goal). Hereafter, each of the three basic phases of a single generation is described from the standpoint of the EA framework. However, before this, we introduce a concept which is common to all of these phases; that of solution pool processors.