Extension of a relational query language to capture more information from objects with many-many relationships

The focus of this paper is the application of genetic concepts to database query optimization. Usually many decision support applications such as task assignment, truck deliveries, and airline screw scheduling problems usually need to get information from objects with a many-many relationship. However, current relational operators including the complete set of relational algebraic and other relational operators are difficult to get required information from objects with a many-many relationship. In this paper, we extend SQL so that users can capture more information from objects with a many-many relationship by using the query language directly. The relational operators were extended. Some of these operators may take a very long time to find an optimal solution. Genetic algorithms are developed to find the near-optimal solution of this kind of operators. The computational effort involved in the algorithms is bounded by a polynomial time.