Genetic Programming for Multiscale Modeling

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Kumara Sastry and D. D. Johnson and 
                 David E. Goldberg and Pascal Bellon",
  title =        "Genetic Programming for Multiscale Modeling",
  journal =      "International Journal for Multiscale Computational
  year =         "2004",
  volume =       "2",
  number =       "2",
  month =        "1 " # jun,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1543-1649",
  DOI =          "doi:10.1615/IntJMultCompEng.v2.i2.50",
  size =         "19 pages",
  abstract =     "We propose the use of genetic programming (GP) a
                 genetic algorithm that evolves computer programs for
                 bridging simulation methods across multiple scales of
                 time and/or length. The effectiveness of genetic
                 programming in multiscale simulation is demonstrated
                 using two illustrative, non-trivial case studies in
                 science and engineering. The first case is
                 multi-timescale materials kinetics modelling, where
                 genetic programming is used to symbolically regress a
                 mapping of all diffusion barriers from only a few
                 calculated ones, thereby avoiding explicit calculation
                 of all the barriers. The GP-regressed barrier function
                 enables use of kinetic Monte Carlo for realistic
                 potentials and simulation of realistic experimental
                 times (seconds). Specifically, a GP regression is
                 applied to vacancy-assisted migration on a surface of a
                 binary alloy and predict the diffusion barriers within
                 0.1-1percent error using 3percent (or less) of the
                 barriers. The second case is the development of
                 constitutive relation between macroscopic variables
                 using measured data, where GP is used to evolve both
                 the function form of the constitutive equation as well
                 as the coefficient values. Specifically, GP regression
                 is used for developing a constitutive relation between
                 flow stress and temperature-compensated strain rate
                 based on microstructural characterisation for an
                 aluminium alloy AA7055. We not only reproduce a
                 constitutive relation proposed in literature, but also
                 develop a new constitutive equation that fits both
                 low-strain-rate and high-strain-rate data. We hope
                 these disparate example applications exemplify the
                 power of GP for multiscaling at the price, of course,
                 of not knowing physical details at the intermediate

Genetic Programming entries for Kumara Sastry Duane D Johnson David E Goldberg Pascal Bellon