Epigenetic programming: Genetic programming incorporating epigenetic learning through modification of histones

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

@Article{Tanev:2008:IS,
  author =       "Ivan Tanev and Kikuo Yuta",
  title =        "Epigenetic programming: Genetic programming
                 incorporating epigenetic learning through modification
                 of histones",
  journal =      "Information Sciences",
  year =         "2008",
  volume =       "178",
  number =       "23",
  pages =        "4469--4481",
  month =        "1 " # dec,
  note =         "Special Section: Genetic and Evolutionary Computing",
  keywords =     "genetic algorithms, genetic programming, epigenesis,
                 learning histone code",
  ISSN =         "0020-0255",
  DOI =          "doi:10.1016/j.ins.2008.07.027",
  size =         "13 pages",
  abstract =     "We present the results of our work in simulating the
                 recently discovered findings in molecular biology
                 regarding the significant role which histones play in
                 regulating the gene expression in eukaryotes. Extending
                 the notion of inheritable genotype in evolutionary
                 computation from the commonly considered model of DNA
                 to chromatin (DNA and histones), we present epigenetic
                 programming as an approach, incorporating an explicitly
                 controlled gene expression through modification of
                 histones in strongly-typed genetic programming (STGP).
                 We propose a double cell representation of the
                 simulated individuals, comprising somatic cell and germ
                 cell, both represented by their respective chromatin
                 structures. Following biologically plausible concepts,
                 we regard the plastic phenotype of the somatic cell,
                 achieved via controlled gene expression owing to
                 modifications to histones (epigenetic learning, EL) as
                 relevant for fitness evaluation, while the genotype of
                 the germ cell corresponds to the phylogenesis of the
                 individuals. The beneficial effect of EL on the
                 performance characteristics of STGP is verified on
                 evolution of social behaviour of a team of predator
                 agents in the predator prey pursuit problem.
                 Empirically obtained performance evaluation results
                 indicate that EL contributes to about 2-fold
                 improvement of computational effort of STGP. We trace
                 the cause for that to the cumulative effect of
                 polyphenism and epigenetic stability, both contributed
                 by EL. The former allows for phenotypic diversity of
                 genotypically similar individuals, while the latter
                 robustly preserves the individuals from the destructive
                 effects of crossover by silencing certain genotypic
                 combinations and explicitly activating them only when
                 they are most likely to be expressed in corresponding
                 beneficial phenotypic traits.",
}

Genetic Programming entries for Ivan T Tanev Kikuo Yuta

Citations