@inproceedings{WZ2009EDAWGPE,
  author    = {Thomas Weise and Michael Zapf},
  title     = {Evolving Distributed Algorithms with Genetic Programming: Election},
  booktitle = {2009 Summit on Genetic and Evolutionary Computing (2009) GEC Summit},
  ISBN      = {978-1-60558-326-6},
  publisher = {Association for Computing Machinery (ACM)},
  year      = {2009},
  month     =  jun # {~14},
  location  = {Hua-Ting Hotel and Towers, Shanghai, China},
  address   = {New York, NY, USA},
  note      = {The work is online available at
               http://www.it-weise.de/documents/index.html\#WZ2009EDAWGPE.\\
               The paper can be downloaded at
               http://www.it-weise.de/documents/files/WZ2009EDAWGPE.pdf.\\
               Contact Thomas Weise at tweise@gmx.de or http://www.it-weise.de/.},
  abstract  = {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.},
  contents  = {1. Introduction\\
               2. Why Genetic Programming is Difficult\\
               2.1. Premature Convergence\\
               2.2. Robustness\\
               2.3. Overfitting\\
               2.4. Ruggedness and Weak Causality\\
               2.5. Neutrality and Code Bloat\\
               2.6. Needle-In-A-Haystack Problems\\
               2.7. Epistasis\\
               2.8. Correctness\\
               3. Approaches\\
               3.1. Standard Genetic Programming with Memory [SGP]\\
               3.1.1. Memory in the SGP Approach\\
               3.2. Extended Standard Genetic Programming with Indexed Memory [eSGP]\\
               3.3. Linear Genetic Programming [LGP]\\
               3.4. Fraglets\\
               3.5. Rule-based Genetic Programming [RBGP]\\
               3.5.1. Genotype and Phenotype\\
               3.5.2. Levels of Independence\\
               3.6. Extended Rule-based Genetic Programming [eRBGP]\\
               4. Evolving Election Algorithms\\
               4.1. Objective Function\\
               4.2. Experimental Settings\\
               4.3. Evolved Algorithms\\
               4.4. Results\\
               5. Conclusions\\
               References\\
               (Citation Suggestion)\\
               },
  keywords  = {Network Protocols, Genetic Programming, eRBGP, RBGP, Rule-based Genetic
               Programming, Election, LGP, Linear Genetic Programming, SGP, Standard
               Genetic Programming, Fraglets},
  language  = {en},
  url       = {http://www.it-weise.de/documents/index.html\#WZ2009EDAWGPE}
}

back to document list