Automated design of random dynamic graph models for enterprise computer network applications

0Citations
Citations of this article
5Readers
Mendeley users who have this article in their library.

Abstract

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.

Cite

CITATION STYLE

APA

Pope, A. S., Tauritz, D. R., & Rawlings, C. (2019). Automated design of random dynamic graph models for enterprise computer network applications. In GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 352–353). Association for Computing Machinery, Inc. https://doi.org/10.1145/3319619.3322049

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free