Handling incomplete information in an evolutionary environment

In this paper we address the role of modelling creativity in Artificial Intelligence using a Genetic or Evolutionary paradigm approach, and the Knowledge Representation of the universe of discourse using Extended Logic Programming paradigm to handle with incomplete information in evolutionary environments. We present a new computational model approach for the creation of evolutive systems that materializes the knowledge representation and symbolic reasoning mechanisms. Using the evolutionary programming paradigm, the candidate solutions are seen as evolutionary logic programs or theories, being the test whether a solution is optimal based on a measure of the quality-of- information carried out by those logical theories or programs. From the point of view of the process, the quantification of the universe of discourse optimized along the time, will permit the achievement of the best mathematical logical theory (given in terms of logic programs) and consequently the best modulation of the system to solve a particular problem