Sensible Initialization Using Expert Knowledge for Genome-Wide Analysis of Epistasis Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Casey S. Greene and Bill C. White and Jason H. Moore",
  title =        "Sensible Initialization Using Expert Knowledge for
                 Genome-Wide Analysis of Epistasis Using Genetic
  booktitle =    "2009 IEEE Congress on Evolutionary Computation",
  year =         "2009",
  editor =       "Andy Tyrrell",
  pages =        "1289--1296",
  address =      "Trondheim, Norway",
  month =        "18-21 " # may,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-2959-2",
  file =         "P152.pdf",
  DOI =          "doi:10.1109/CEC.2009.4983093",
  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
  keywords =     "genetic algorithms, genetic programming",
  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