Automated Design of Random Dynamic Graph Models for Enterprise Computer Network Applications

被引：0
|
作者：
Pope, Aaron Scott [1 ]
Tauritz, Daniel R. [1 ]
Rawlings, Chris [2 ]
机构：
[1] Missouri Univ Sci & Technol, Dept Comp Sci, Rolla, MO 65409 USA
[2] Los Alamos Natl Lab, Los Alamos, NM USA
来源：
PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION (GECCCO'19 COMPANION) | 2019年
关键词：
Random graph models; dynamic graphs; genetic programming;
D O I：
10.1145/3319619.3322049
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
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.
引用
收藏
页码：352 / 353
页数：2
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