Genetic Algorithms and Genetic Programming for Multiscale Modeling: Applications in Materials Science and Chemistry and Advances in Scalability

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Sastry:thesis,
  author =       "Kumara Narasimha Sastry",
  title =        "Genetic Algorithms and Genetic Programming for
                 Multiscale Modeling: Applications in Materials Science
                 and Chemistry and Advances in Scalability",
  school =       "University of Illinois at Urbana-Champaign",
  year =         "2007",
  address =      "Urbana IL., USA",
  month =        "13 " # sep,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://phdtree.org/pdf/25454123-genetic-algorithms-and-genetic-programming-for-multiscale-modeling-applications-in-materials-science-and-chemistry-and-advances-in-scalabilit/",
  URL =          "http://www.kumarasastry.com/2007/09/13/genetic-algorithms-and-genetic-programming-for-multiscale-modeling-applications-in-materials-science-and-chemistry-and-advances-in-scalability/",
  URL =          "http://hdl.handle.net/2142/87085",
  URL =          "http://adsabs.harvard.edu/abs/2007PhDT.......323S",
  URL =          "http://www.illigal.uiuc.edu/pub/papers/IlliGALs/2007019.pdf",
  size =         "249 pages",
  abstract =     "Effective and efficient multiscale modeling is
                 essential to advance both the science and synthesis in
                 a wide array of fields such as physics, chemistry,
                 materials science, biology, biotechnology and
                 pharmacology. This study investigates the efficacy and
                 potential of using genetic algorithms for multiscale
                 materials modelling and addresses some of the
                 challenges involved in designing competent algorithms
                 that solve hard problems quickly, reliably and
                 accurately. In particular, this thesis demonstrates the
                 use of genetic algorithms (GAs) and genetic programming
                 (GP) in multiscale modelling with the help of two
                 non-trivial case studies in materials science and
                 chemistry. The first case study explores the utility of
                 genetic programming (GP) in multi-timescaling alloy
                 kinetics simulations. In essence, GP is used to bridge
                 molecular dynamics and kinetic Monte Carlo methods to
                 span orders-of-magnitude in simulation time.
                 Specifically, GP is used to regress symbolically an
                 inline barrier function from a limited set of molecular
                 dynamics simulations to enable kinetic Monte Carlo that
                 simulate seconds of real time. Results on a non-trivial
                 example of vacancy-assisted migration on a surface of a
                 face-centered cubic (fcc) Copper-Cobalt (CuxCo1-x)
                 alloy show that GP predicts all barriers with
                 0.1percent error from calculations for less than
                 3percent of active configurations, independent of type
                 of potentials used to obtain the learning set of
                 barriers via molecular dynamics. The resulting method
                 enables 2-9 orders-of-magnitude increase in real-time
                 dynamics simulations taking 4-7 orders-of-magnitude
                 less CPU time. The second case study presents the
                 application of multiobjective genetic algorithms
                 (MOGAs) in multiscaling quantum chemistry simulations.
                 Specifically, MOGAs are used to bridge high-level
                 quantum chemistry and semiempirical methods to provide
                 accurate representation of complex molecular
                 excited-state and ground-state behaviour. Results on
                 ethylene and benzene - two common building-blocks in
                 organic chemistry - indicate that MOGAs produce
                 high-quality semi-empirical methods that (1) are stable
                 to small perturbations, (2) yield accurate
                 configuration energies on untested and critical excited
                 states, and (3) yield ab initio quality excited-state
                 dynamics. The proposed method enables simulations of
                 more complex systems to realistic multi-picosecond
                 timescales, well beyond previous attempts or
                 expectation of human experts, and 2-3
                 orders-of-magnitude reduction in computational cost.",
  abstract =     "While the two applications use simple evolutionary
                 operators, in order to tackle more complex systems,
                 their scalability and limitations have to be
                 investigated. The second part of the thesis addresses
                 some of the challenges involved with a successful
                 design of genetic algorithms and genetic programming
                 for multiscale modeling. The first issue addressed is
                 the scalability of genetic programming, where facetwise
                 models are built to assess the population size required
                 by GP to ensure adequate supply of raw building blocks
                 and also to ensure accurate decision-making between
                 competing building blocks.

                 This study also presents a design of competent genetic
                 programming, where traditional fixed recombination
                 operators are replaced by building and sampling
                 probabilistic models of promising candidate programs.
                 The proposed scalable GP, called extended compact GP
                 (eCGP), combines the ideas from extended compact
                 genetic algorithm (eCGA) and probabilistic incremental
                 program evolution (PIPE) and adaptively identifies,
                 propagates and exchanges important subsolutions of a
                 search problem. Results show that eCGP scales cubically
                 with problem size on both GP-easy and GP-hard
                 problems.

                 Finally, facet-wise models are developed to explore
                 limitations of scalability of MOGAs, where the
                 scalability of multiobjective algorithms in reliably
                 maintaining Pareto-optimal solutions is addressed. The
                 results show that even when the building blocks are
                 accurately identified, massive multimodality of the
                 search problems can easily overwhelm the nicher
                 (diversity preserving operator) and lead to exponential
                 scale-up. Facet wise models are developed, which
                 incorporate the combined effects of model accuracy,
                 decision making, and sub-structure supply, as well as
                 the effect of niching on the population sizing, to
                 predict a limit on the growth rate of a maximum number
                 of sub-structures that can compete in the two
                 objectives to circumvent the failure of the niching
                 method. The results show that if the number of
                 competing building blocks between multiple objectives
                 is less than the proposed limit, multiobjective GAs
                 scale-up polynomially with the problem size on
                 boundedly-difficult problems.",
  notes =        "Available as IlliGAL technical report No. 2007019",
}

Genetic Programming entries for Kumara Sastry

Citations