The real life provides a large range of problems, which cannot be solved in any traditional way. An efficient approach of solving them is to use genetic algorithms. Genetic algorithms represent a search method that can be used for both solving problems and modeling evolutionary systems. Genetic algorithms are implemented as a simulation in which a population of representations of candidate solutions to a problem evolves toward better solutions. All the traditional approaches are based on evolution only, while the proposed approach will try to integrate learning in the evolutionary process.
