Genetic algorithms and genetic programming for multiscale materials modeling: Applications and advances in scalability
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@PhdThesis{Sastry:thesis,
author = "Kumara Narasimha Sastry",
title = "Genetic algorithms and genetic programming for
multiscale materials modeling: Applications 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://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