Development and Evaluation of an Open-Ended Computational Evolution System for the Genetic Analysis of Susceptibility to Common Human Diseases

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{conf/evoW/MooreABW08,
  title =        "Development and Evaluation of an Open-Ended
                 Computational Evolution System for the Genetic Analysis
                 of Susceptibility to Common Human Diseases",
  author =       "Jason H. Moore and Peter C. Andrews and 
                 Nate Barney and Bill C. White",
  bibdate =      "2008-04-15",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/evoW/evobio2008.html#MooreABW08",
  booktitle =    "Proceedings of the 6th European Conference, on
                 Evolutionary Computation, Machine Learning and Data
                 Mining in Bioinformatics, Evo{BIO} 2008",
  publisher =    "Springer",
  year =         "2008",
  volume =       "4973",
  editor =       "Elena Marchiori and Jason H. Moore",
  isbn13 =       "978-3-540-78756-3",
  pages =        "129--140",
  series =       "Lecture Notes in Computer Science",
  DOI =          "doi:10.1007/978-3-540-78757-0_12",
  address =      "Naples, Italy",
  month =        mar # " 26-28",
  keywords =     "genetic algorithms, genetic programming, computational
                 evolution",
  abstract =     "An important goal of human genetics is to identify DNA
                 sequence variations that are predictive of
                 susceptibility to common human diseases. This is a
                 classification problem with data consisting of discrete
                 attributes and a binary outcome. A variety of different
                 machine learning methods based on artificial evolution
                 have been developed and applied to modelling the
                 relationship between genotype and phenotype. While
                 artificial evolution approaches show promise, they are
                 far from perfect and are only loosely based on real
                 biological and evolutionary processes. It has recently
                 been suggested that a new paradigm is needed where
                 artificial evolution is transformed to computational
                 evolution (CE) by incorporating more biological and
                 evolutionary complexity into existing algorithms. It
                 has been proposed that CE systems will be more likely
                 to solve problems of interest to biologists and
                 biomedical researchers. The goal of the present study
                 was to develop and evaluate a prototype CE system for
                 the analysis of human genetics data. We describe here
                 this new open-ended CE system and provide initial
                 results from a simulation study that suggests more
                 complex operators result in better solutions.",
}

Genetic Programming entries for Jason H Moore Peter C Andrews Nate Barney Bill C White

Citations