Environmental Sensing of Expert Knowledge in a Computational Evolution System for Complex Problem Solving in Human Genetics

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Casey S. Greene and Douglas P. Hill and 
                 Jason H. Moore",
  title =        "Environmental Sensing of Expert Knowledge in a
                 Computational Evolution System for Complex Problem
                 Solving in Human Genetics",
  booktitle =    "Genetic Programming Theory and Practice {VII}",
  year =         "2009",
  editor =       "Rick L. Riolo and Una-May O'Reilly and 
                 Trent McConaghy",
  series =       "Genetic and Evolutionary Computation",
  address =      "Ann Arbor",
  month =        "14-16 " # may,
  publisher =    "Springer",
  chapter =      "2",
  pages =        "19--36",
  keywords =     "genetic algorithms, genetic programming, Genetic
                 Epidemiology, Symbolic Discriminant Analysis,
  isbn13 =       "978-1-4419-1653-2",
  DOI =          "doi:10.1007/978-1-4419-1626-6_2",
  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.",
  notes =        "part of \cite{Riolo:2009:GPTP}",

Genetic Programming entries for Casey S Greene Douglas P Hill Jason H Moore