Inheritable Epigenetics in Genetic Programming

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  author =       "William {La Cava} and Lee Spector",
  title =        "Inheritable Epigenetics in Genetic Programming",
  booktitle =    "Genetic Programming Theory and Practice XII",
  year =         "2014",
  editor =       "Rick Riolo and William P. Worzel and Mark Kotanchek",
  series =       "Genetic and Evolutionary Computation",
  pages =        "37--51",
  address =      "Ann Arbor, USA",
  month =        "8-10 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Epigenetics,
                 Symbolic regression",
  isbn13 =       "978-3-319-16029-0",
  DOI =          "doi:10.1007/978-3-319-16030-6_3",
  abstract =     "Classical genetic programming solves problems by
                 applying the Darwinian concepts of selection, survival
                 and reproduction to a population of computer programs.
                 Here we extend the biological analogy to incorporate
                 epigenetic regulation through both learning and
                 evolution. We begin the chapter with a discussion of
                 Darwinian, Lamarckian, and Baldwinian approaches to
                 evolutionary computation and describe how recent
                 findings in biology differ conceptually from the
                 computational strategies that have been proposed. Using
                 inheritable Lamarckian mechanisms as inspiration, we
                 propose a system that allows for updating of
                 individuals in the population during their lifetime
                 while simultaneously preserving both genotypic and
                 phenotypic traits during reproduction. The
                 implementation is made simple through the use of
                 syntax-free, developmental, linear genetic programming.
                 The representation allows for arbitrarily-ordered
                 genomes to be syntactically valid programs, thereby
                 creating a genetic programming approach upon which
                 quasi-uniform epigenetic updating and inheritance can
                 easily be applied. Generational updates are made using
                 an epigenetic hill climber (EHC), and the epigenetic
                 properties of genes are inherited during crossover and
                 mutation. The addition of epigenetics results in faster
                 convergence, less bloat, and an improved ability to
                 find exact solutions on a number of symbolic regression
  notes =        "

                 Part of \cite{Riolo:2014:GPTP} published after the
                 workshop in 2015",

Genetic Programming entries for William La Cava Lee Spector