ABSTRACT
The techniques used to solve difficult combinatorial optimization problems have evolved from constructive algorithms to local search techniques, and finally to population-based algorithms. Population-based methods have become very popular. They provide good solutions since any constructive method can be used to generate the initial population, and any local search technique can be used to improve each solution in the population. But population-based methods have the additional advantage of being able to combine good solutions in order to get possibly better ones. The basic idea behind this way of doing is that good solutions often share parts with optimal solutions (Hertz and Kobler, 1998).
