Evolving random graph generators: A case for increased algorithmic primitive granularity

Abstract

Random graph generation techniques provide an invaluable tool for studying graph related concepts. Unfortunately, traditional random graph models tend to produce artificial representations of real-world phenomenon. Manually developing customized random graph models for every application would require an unreasonable amount of time and effort. In this work, a platform is developed to automate the production of random graph generators that are tailored to specific applications. Elements of existing random graph generation techniques are used to create a set of graph-based primitive operations. A hyper-heuristic approach is employed that uses genetic programming to automatically construct random graph generators from this set of operations. This work improves upon similar research by increasing the level of algorithmic sophistication possible with evolved solutions, allowing more accurate modeling of subtle graph characteristics. The versatility of this approach is tested against existing methods and experimental results demonstrate the potential to outperform conventional and state of the art techniques for specific applications.