Integrating Genetic Algorithm and Rule Based Systems

Genetic algorithms may be considering from two different points of view: as global optimization procedure and learning procedure. As global optimization procedure they analyze parallel search space and are able to escape from local extreme. They are also applicable if target value is not known in advance or cannot be defined. This paper is presenting an example of genetic algorithms where the expected results are a set of efficient frontiers and decision maker chooses acceptable values among possible solutions. The outcomes of implementing genetic algorithm are inputs into knowledge base which helps decision maker to make final judgment. The paper also includes the software solution for genetic algorithms. The integration of genetic algorithms and rule based systems has shown satisfactory development and applicable power.