Using Genetic Programming for an Advanced Performance Assessment of Industrially Relevant Heterogeneous Catalysts
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{BBSTLCC09,
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author = "L. A. Baumes and A. Blansche and P. Serna and
A. Tchougang and N. Lachiche and P. Collet and A. Corma",
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title = "Using Genetic Programming for an Advanced Performance
Assessment of Industrially Relevant Heterogeneous
Catalysts",
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journal = "Materials and Manufacturing Processes",
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year = "2009",
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volume = "24",
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number = "3",
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pages = "282--292",
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month = mar,
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keywords = "genetic algorithms, genetic programming, Data mining,
Heterogeneous catalysis, High-throughput, Materials
science",
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ISSN = "1042-6914",
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publisher = "Taylor and Francis",
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URL = "http://lsiit.u-strasbg.fr/Publications/2009/BBSTLCC09",
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DOI = "doi:10.1080/10426910802679196",
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size = "11 pages",
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abstract = "Beside the ease and speed brought by automated
synthesis stations and reactors technologies in
materials science, adapted informatics tools must be
further developed in order to handle the increase of
throughput and data volume, and not to slow down the
whole process. This article reports the use of genetic
programming (GP) in heterogeneous catalysis. Despite
the fact that GP has received only little attention in
this domain, it is shown how such an approach can be
turned into a very singular and powerful tool for solid
optimization, discovery, and monitoring. Jointly with
neural networks, the GP paradigm is employed in order
to accurately and automatically estimate the whole
curve conversion vs. time in the epoxidation of large
olefins using titanosilicates, Ti-MCM-41 and Ti-ITQ-2,
as catalysts. In contrast to previous studies in
combinatorial materials science and high-throughput
screening, it was possible to estimate the entire
evolution of the catalytic reaction for unsynthesized
catalysts. Consequently, the evaluation of the
performance of virtual solids is not reduced to a
single point (e.g., the conversion level at only one
given reaction time or the initial reaction rate). The
methodology is thoroughly detailed, while stressing on
the comparison between the recently proposed Context
Aware Crossover (CAX) and the traditional crossover
operator.",
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notes = "Affiliations: Institute of Chemical Technology,
CSIC-UPV, Valencia, Spain
Louis Pasteur University, LSIIT, FDBT, Illkirch,
France",
- }
Genetic Programming entries for
Laurent Allan Baumes
Alexandre Blansche
Pedro Serna Ros
Ariel Tchougang
Nicolas Lachiche
Pierre Collet
Avelino Corma Canos
Citations