The MR-GAP extends this concept by assigning each agent multiple resources that are consumed in the accomplishment of the assigned task. This extension is significant in that it allows the modeling of more complex models reflecting the more typical problems faced in real life problems. The MR-GAP has been used in a wide variety of applications ranging from solving truck routing problems to planning the location of distributed databases. Potential applications include transportation and telecommunications network design, cargo loading, warehouse design, work planning, course scheduling and classroom assignment. The GA is a meta-heuristic that directs a global search of the solution space to find the best possible solution.
GAs are a weak method, meaning that they make few assumptions about the problem domain.
Weak methods are often low power in that they are not very efficient. Genetic Algorithms represent an apparent anomaly, a powerful weak method. IF elitist replacement strategy ensure that best of old population is moved to new population. The GA is an attempt to solve complex problems using simple building blocks. Modeled after general evolutionary theory, this approach seeks to emulate the robustness found in natures survivors.