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.
GAs concurrently develop a population of potential solutions to the given problem.
GAs are somewhat unique in that to solve a given problem, it must be able to evaluate potential solution but, it does not necessarily require knowledge of how to solve the problem. The first design issue is to select an encoding that will capture the critical characteristics of the problem to be solved. The chromosome is the string of bits or integers that encode the solution. Each bit or string is a gene and the value of a given gene is commonly called an allele. To solve the MR-GAP, the critical problem characteristic is the assignment of agents to tasks.