Fossa: Using genetic programming to learn ECA rules for adaptive networking applications

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Froemmgen:2015:ieeeLCN,

• author = "Alexander Froemmgen and Robert Rehner and Max Lehn and Alejandro Buchmann",
• booktitle = "40th IEEE Conference on Local Computer Networks (LCN)",
• title = "Fossa: Using genetic programming to learn ECA rules for adaptive networking applications",
• year = "2015",
• pages = "197--200",
• abstract = "Due to complex interdependencies and feedback loops between network layers and nodes, the development of adaptive applications is difficult. As networking applications respond nonlinearly to changes in the environment and adaptations, defining concrete adaptation rules is nontrivial. In this paper, we present the offline learner Fossa, which uses genetic programming to automatically learn suitable Event Condition Action (ECA) rules. Based on utility functions defined by the developer, the genetic programming learner generates a multitude of rule sets and evaluates them using simulations to obtain their utility. We show, for a concrete example scenario, how the genetic programming learner benefits from the clear model of the ECA rules, and that the methodology efficiently generates ECA rules which outperform nonadaptive and manually tuned solutions.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/LCN.2015.7366305",
• month = oct,
• notes = "Also known as \cite{7366305}",

}

Genetic Programming entries for Alexander Froemmgen Robert Rehner Max Lehn Alejandro Buchmann

Citations