Evolutionary Inference of Biological Systems Accelerated on Graphics Processing Units
Created by W.Langdon from
gp-bibliography.bib Revision:1.8154
- @PhdThesis{Nobile:thesis,
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author = "Marco Salvatore Nobile",
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title = "Evolutionary Inference of Biological Systems
Accelerated on Graphics Processing Units",
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school = "Dipartimento di Informatica, Sistemistica e
Comunicazione Universita degli Studi di
Milano-Bicocca",
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year = "2014",
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address = "Milan, Italy",
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month = oct,
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keywords = "genetic algorithms, genetic programming, Cartesian
genetic programming, particle swarm optimisation, PSO,
GPU, GPGPU, nvidia, CUDA, cupSODA, cuTauLeeping,
cuPEPSO, petri net, MemHPG, Schoegl",
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URL = "https://boa.unimib.it/handle/10281/75434",
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URL = "https://boa.unimib.it/retrieve/handle/10281/75434/111846/PhD_unimib_%20%09603317.pdf",
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size = "318 pages",
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abstract = "In silico analysis of biological systems represents a
valuable alternative and complementary approach to
experimental research. Computational methodologies,
indeed, allow to mimic some conditions of cellular
processes that might be difficult to dissect by
exploiting traditional laboratory techniques, therefore
potentially achieving a thorough comprehension of the
molecular mechanisms that rule the functioning of cells
and organisms. In spite of the benefits that it can
bring about in biology, the computational approach
still has two main limitations: first, there is often a
lack of adequate knowledge on the biological system of
interest, which prevents the creation of a proper
mathematical model able to produce faithful and
quantitative predictions; second, the analysis of the
model can require a massive number of simulations and
calculations, which are computationally burdensome. The
goal of the present thesis is to develop novel
computational methodologies to efficiently tackle these
two issues, at multiple scales of biological complexity
(from single molecular structures to networks of
biochemical reactions). The inference of the missing
data related to the three-dimensional structures of
proteins, the number and type of chemical species and
their mutual interactions, the kinetic parameters is
performed by means of novel methods based on
Evolutionary Computation and Swarm Intelligence
techniques. General purpose GPU computing has been
adopted to reduce the computational time, achieving a
relevant speedup with respect to the sequential
execution of the same algorithms. The results presented
in this thesis show that these novel evolutionary-based
and GPU-accelerated methodologies are indeed feasible
and advantageous from both the points of view of
inference quality and computational performances.",
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notes = "Supervisor: Giancarlo Mauri",
- }
Genetic Programming entries for
Marco Nobile
Citations