Developing Non-linear Rate Constant QSPR using Decision Trees and Multi-Gene Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @InCollection{DATTA:2018:ISPSE,
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author = "Shounak Datta and Vikrant A. Dev and Mario R. Eden",
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title = "Developing Non-linear Rate Constant QSPR using
Decision Trees and Multi-Gene Genetic Programming",
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booktitle = "13th International Symposium on Process Systems
Engineering (PSE 2018)",
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editor = "Mario R. Eden and Marianthi G. Ierapetritou and
Gavin P. Towler",
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series = "Computer Aided Chemical Engineering",
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publisher = "Elsevier",
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volume = "44",
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pages = "2473--2478",
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year = "2018",
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keywords = "genetic algorithms, genetic programming, Multi-gene
genetic programming, hybrid algorithm, nonlinear
regression, machine learning, stochastic optimization",
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ISSN = "1570-7946",
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DOI = "doi:10.1016/B978-0-444-64241-7.50407-9",
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URL = "http://www.sciencedirect.com/science/article/pii/B9780444642417504079",
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abstract = "Developing a QSPR model, which not only captures the
influence of reactant structures but also the solvent
effect on reaction rate, is of significance. Such QSPR
models will serve as a prerequisite for the
simultaneous computer-aided molecular design (CAMD) of
reactants, products and solvents. They will also be
useful in predicting the rate constant without entirely
relying on experiments. To develop such a QSPR,
recently, Datta et al. (2017) used the Diels-Alder
reaction as a case study. Their model displayed great
promise, but, there is scope for improvement in the
model's predictive ability. In our work, we improve
upon their model by introducing non-linearity. This is
achieved using multi-gene genetic programming (MGGP).
In our methodology, a combination of genetic algorithm
(GA) and directed trees was used to develop a branched
version of chromosomes, allowing additional
possibilities in the generated models. In our work,
prior to model development through MGGP, principal
component analysis (PCA) was conducted. Lastly, models
were evaluated based on metrics such as R2, Q2, and
RMSE",
- }
Genetic Programming entries for
Shounak Datta
Vikrant A Dev
Mario R Eden
Citations