Abstract
Weak Pseudo-random Number Generators (PRNGs) can be improved with additional operations and techniques. However, some techniques added to the RNGs in an attempt to improve the quality and randomness of the generator fail to produce the desired results. Literature surveys have produced very few ideas and information related to the constituent steps involved in the individual algorithms used in constructing the PRNGs. In preference to studying and understanding the individual elements of the algorithms, designers have preferred to create new PRNGs and have missed the opportunity to increase the basic understanding of the field. If the building blocks of PRNGs are well understood, along with their interactions, the design of high quality PRNGs is possible. Understanding what works also requires understanding what does not work. Those techniques can then be avoided. This paper is an exploration of some novel techniques for improving the quality of weak PRNGs that failed to live up to the promise of improving PRNGs.