Evolving distributed algorithms with genetic programming: election

In this paper, we present a detailed analysis of the application of Genetic Programming to the evolution of distributed algorithms. This research field has many facets which make it especially difficult. These aspects are discussed and countermeasures are provided. Six different Genetic Programming approaches (SGP, eSGP, LGP, RBGP, eRBGP, and Fraglets) are applied to the election problem as case study utilizing these countermeasures. The results of the experiments are analyzed statistically and discussed thoroughly. Categories and Subject Descriptors C.2.2 [Network Protocols]: Applications; I.2.2 [Automatic Programming]: Program Synthesis General Terms Algorithms, Design Preview This document is a preview version and not necessarily identical with the original. http://www.it-weise.de/

• Type: Conference
• Year: 2009
• Where: GECCO
• Authors: Thomas Weise, Michael Zapf
• Keywords: Different Genetic Programming, GECCO 2009, Genetic Programming, Optimization, Preview This Document