- author = "Casey S. Greene and Bill C. White and Jason H. Moore",
- title = "Using expert knowledge in initialization for genome-wide analysis of epistasis using genetic programming",
- booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation",
- year = "2008",
- editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener",
- isbn13 = "978-1-60558-130-9",
- pages = "351--352",
- address = "Atlanta, GA, USA",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p351.pdf",
-
DOI = "
doi:10.1145/1389095.1389158",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- month = "12-16 " # jul,
- abstract = "In human genetics it is now possible to measure large numbers of DNA sequence variations across the human genome. Given current knowledge about biological networks and disease processes it seems likely that disease risk can best be modelled by interactions between biological components, which may be examined as interacting DNA sequence variations. The machine learning challenge is to effectively explore interactions in these datasets to identify combinations of variations which are predictive of common human diseases. Genetic programming is a promising approach to this problem. The goal of this study is to examine the role that an expert knowledge aware initialiser can play in the framework of genetic programming. We show that this expert knowledge aware initializer outperforms both a random initializer and an enumerative initialiser.",
- keywords = "genetic algorithms, genetic programming, expert knowledge, genetic analysis, Initialisation, Bioinformatics, computational biology: Poster, TuRF, Relief, SNP, MDR, SDA",
-
notes = "GECCO-2008 A joint meeting of the seventeenth
international conference on genetic algorithms
(ICGA-2008) and the thirteenth annual genetic
programming conference (GP-2008).
ACM Order Number 910081. Also known as \cite{1389158}
Comparison of three ways of loading problem inputs (10000+) into initial population to predict clinical end point (death). Artificial datasets.",
