- author = "Alexander Bailey and Mario Ventresca and Beatrice Ombuki-Berman",
- title = "Automatic generation of graph models for complex networks by genetic programming",
- booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference",
- year = "2012",
- editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb",
- isbn13 = "978-1-4503-1177-9",
- pages = "711--718",
- keywords = "genetic algorithms, genetic programming",
- month = "7-11 " # jul,
- organisation = "SIGEVO",
- address = "Philadelphia, Pennsylvania, USA",
-
URL = "
http://cs.adelaide.edu.au/~brad/papers/alexanderThielPeacock.pdf",
-
DOI = "
doi:10.1145/2330163.2330263",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- abstract = "Complex networks have attracted a large amount of research attention, especially over the past decade, due to their prevalence and importance in our daily lives. Numerous human-designed models have been proposed that aim to capture and model different network structures, for the purpose of improving our understanding the real-life phenomena and its dynamics in different situations. Groundbreaking work in genetics, medicine, epidemiology, neuroscience, telecommunications, social science and drug discovery, to name some examples, have directly resulted. Because the graph models are human made (a very time consuming process) using a small subset of example graphs, they often exhibit inaccuracies when used to model similar structures. This paper represents the first exploration into the use of genetic programming for automating the discovery and algorithm design of graph models, representing a totally new approach with great interdisciplinary application potential. We present exciting initial results that show the potential of GP to replicate existing complex network algorithms.",
-
notes = "alexanderThielPeacock.pdf corrected version (fixed
typo in background resistivity)
Entered for 2013 HUMIES GECCO 2013
Also known as \cite{2330263} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)",
