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

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Alexander Froemmgen and Robert Rehner and Max Lehn and 
                 Alejandro Buchmann",
  booktitle =    "40th IEEE Conference on Local Computer Networks
  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