Environmental Sensing of Expert Knowledge in a Computational Evolution System for Complex Problem Solving in Human Genetics
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- @InCollection{Greene:2009:GPTP,
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author = "Casey S. Greene and Douglas P. Hill and
Jason H. Moore",
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title = "Environmental Sensing of Expert Knowledge in a
Computational Evolution System for Complex Problem
Solving in Human Genetics",
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booktitle = "Genetic Programming Theory and Practice {VII}",
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year = "2009",
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editor = "Rick L. Riolo and Una-May O'Reilly and
Trent McConaghy",
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series = "Genetic and Evolutionary Computation",
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address = "Ann Arbor",
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month = "14-16 " # may,
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publisher = "Springer",
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chapter = "2",
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pages = "19--36",
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keywords = "genetic algorithms, genetic programming, Genetic
Epidemiology, Symbolic Discriminant Analysis,
Epistasis",
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isbn13 = "978-1-4419-1653-2",
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DOI = "doi:10.1007/978-1-4419-1626-6_2",
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abstract = "The relationship between interindividual variation in
our genomes and variation in our susceptibility to
common diseases is expected to be complex with multiple
interacting genetic factors. A central goal of human
genetics is to identify which DNA sequence variations
predict disease risk in human populations. Our success
in this endeavour will depend critically on the
development and implementation of computational
intelligence methods that are able to embrace, rather
than ignore, the complexity of the genotype to
phenotype relationship. To this end, we have developed
a computational evolution system (CES) to discover
genetic models of disease susceptibility involving
complex relationships between DNA sequence variations.
The CES approach is hierarchically organised and is
capable of evolving operators of any arbitrary
complexity. The ability to evolve operators
distinguishes this approach from artificial evolution
approaches using fixed operators such as mutation and
recombination. Our previous studies have shown that a
CES that can use expert knowledge about the problem in
evolved operators significantly outperforms a CES
unable to use this knowledge. This environmental
sensing of external sources of biological or
statistical knowledge is important when the search
space is both rugged and large as in the genetic
analysis of complex diseases. We show here that the CES
is also capable of evolving operators which exploit one
of several sources of expert knowledge to solve the
problem. This is important for both the discovery of
highly fit genetic models and because the particular
source of expert knowledge used by evolved operators
may provide additional information about the problem
itself. This study brings us a step closer to a CES
that can solve complex problems in human genetics in
addition to discovering genetic models of disease.",
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notes = "part of \cite{Riolo:2009:GPTP}",
- }
Genetic Programming entries for
Casey S Greene
Douglas P Hill
Jason H Moore
Citations