Genetic algorithms are different from other heuristic methods in several ways. The most important difference is that a GA works on a population of possible solutions, while other heuristic methods use a single solution in their iterations. Another difference is that a GA is a probabilistic approach, and not deterministic. Each individual in the GA population represents a possible solution to the problem. The suggested solution is coded into the “genes” of the individual. The values and their position in the “gene string” tell the genetic algorithm what solution the individual represents.