- 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",
- broken = "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",
