Automated Design of Random Dynamic Graph Models for Enterprise Computer Network Applications
Dynamic graphs are an essential tool for representing a wide variety of concepts that change over time. In the case of static graph representations, random graph models are often useful for analyzing and predicting the characteristics of a given network. Even though random dynamic graph models are a trending research topic, the field is still relatively unexplored. The selection of available models is limited and manually developing a model for a new application can be difficult and time-consuming. This work leverages hyper-heuristic techniques to automate the design of novel random dynamic graph models. A genetic programming approach is used to evolve custom heuristics that emulate the behavior of real-world dynamic networks.
A. S. Pope et al., "Automated Design of Random Dynamic Graph Models for Enterprise Computer Network Applications," GECCO 2019 Companion -- Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion, pp. 352 - 353, Association for Computing Machinery (ACM), Jul 2019.
The definitive version is available at https://doi.org/10.1145/3319619.3322049
2019 Genetic and Evolutionary Computation Conference, GECCO 2019 (2019: Jul. 13-17, Prague, Czech Republic)
Keywords and Phrases
Dynamic graphs; Genetic programming; Random graph models
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2019 Association for Computing Machinery (ACM), All rights reserved.
01 Jul 2019