An Intelligent Optimal Genetic Model to Investigate the User Usage Behaviour on World Wide Web

The unexpected wide spread use of WWW and dynamically increasing nature of the web creates new challenges in the web mining since the data in the web inherently unlabelled, incomplete, non linear, and heterogeneous. The investigation of user usage behaviour on WWW is real time problem which involves multiple conflicting measures of performance. These measures make not only computational intensive butalso needs to the possibility of be unable to find the exact solution. Unfortunately, the conventional methods are limited to optimization problems due to the absence of semantic certainty and presence of human intervention. In handling such data and overcome the limitations of conventional methodologies it is necessary to use a soft computing model that can work intelligently to attain optimal solution. To achieve the optimized solution for investigating the web user usage behaviour, the authors in the present paper proposes an Intelligent Optimal Genetic Model, IOGM, which is designed as an optimization tool based on the concept of natural genetic systems. Initially, IOGM comprise a set of individual solutions or chromosomes called the initial population. Later, biologically inspired operators create a new and potentially better population. Finally, by the theory of evolution, survive only optimal individuals from the population and then generate the next biological population. This process is terminated as when an acceptable optimal set of visited patterns is found or after fixed time limit. Additionally, IOGM strengthen by its ability to estimate the optimal stopping time of process. The proposed soft computing model ensures the identifiable features like learning, adaptability, self-maintenance and self-improvement. To validate the proposed system, several experiments were conducted and results proven this are claimed in this paper