Sensible Initialization Using Expert Knowledge for Genome-Wide Analysis of Epistasis Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Greene:2009:cec2,
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author = "Casey S. Greene and Bill C. White and Jason H. Moore",
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title = "Sensible Initialization Using Expert Knowledge for
Genome-Wide Analysis of Epistasis Using Genetic
Programming",
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booktitle = "2009 IEEE Congress on Evolutionary Computation",
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year = "2009",
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editor = "Andy Tyrrell",
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pages = "1289--1296",
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address = "Trondheim, Norway",
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month = "18-21 " # may,
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organization = "IEEE Computational Intelligence Society",
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publisher = "IEEE Press",
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isbn13 = "978-1-4244-2959-2",
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file = "P152.pdf",
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DOI = "doi:10.1109/CEC.2009.4983093",
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abstract = "For biomedical researchers it is now possible to
measure large numbers of DNA sequence variations across
the human genome. Measuring hundreds of thousands of
variations is now routine, but single variations which
consistently predict an individual's risk of common
human disease have proven elusive. Instead of single
variants determining the risk of common human diseases,
it seems more likely that disease risk is best modeled
by interactions between biological components. The
evolutionary computing challenge now is to effectively
explore interactions in these large datasets and
identify combinations of variations which are robust
predictors of common human diseases such as bladder
cancer. One promising approach to this problem is
genetic programming (GP). A GP approach for this
problem will use Darwinian inspired evolution to evolve
programs which find and model attribute interactions
which predict an individual's risk of common human
diseases. The goal of this study is to develop and
evaluate two initializers for this domain. We develop a
probabilistic initializer which uses expert knowledge
to select attributes and an enumerative initializer
which maximizes attribute diversity in the generated
population.We compare these initializers to a random
initializer which displays no preference for
attributes. We show that the expert-knowledge-aware
probabilistic initializer significantly outperforms
both the random initializer and the enumerative
initializer.We discuss implications of these results
for the design of GP strategies which are able to
detect and characterize predictors of common human
diseases.",
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keywords = "genetic algorithms, genetic programming",
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notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and
the IET. IEEE Catalog Number: CFP09ICE-CDR",
- }
Genetic Programming entries for
Casey S Greene
Bill C White
Jason H Moore
Citations