Evolutionary optimization of flavors
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Veeramachaneni:2010:gecco,
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author = "Kalyan Veeramachaneni and Katya Vladislavleva and
Matt Burland and Jason Parcon and Una-May O'Reilly",
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title = "Evolutionary optimization of flavors",
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booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
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year = "2010",
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editor = "Juergen Branke and Martin Pelikan and Enrique Alba and
Dirk V. Arnold and Josh Bongard and
Anthony Brabazon and Juergen Branke and Martin V. Butz and
Jeff Clune and Myra Cohen and Kalyanmoy Deb and
Andries P Engelbrecht and Natalio Krasnogor and
Julian F. Miller and Michael O'Neill and Kumara Sastry and
Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and
Carsten Witt",
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isbn13 = "978-1-4503-0072-8",
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pages = "1291--1298",
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keywords = "genetic algorithms, genetic programming, Real world
applications",
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month = "7-11 " # jul,
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organisation = "SIGEVO",
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address = "Portland, Oregon, USA",
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DOI = "doi:10.1145/1830483.1830713",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "We have acquired panelist data that provides hedonic
(liking) ratings for a set of 40 flavors each composed
of the same 7 ingredients at different concentration
levels. Our goal is to use this data and predict other
flavors, composed of the same ingredients in new
combinations, which the panelist will like. We describe
how we first employ Pareto-Genetic Programming (GP) to
generate a surrogate for the human panelist from the 40
observations. This surrogate, in fact an ensemble of GP
symbolic regression models, can predict liking scores
for flavors outside the observations and provide a
confidence in the prediction. We then employ a
multi-objective particle swarm optimisation (MOPSO) to
design a well and consistently liked flavor suite for a
panelist. The MOPSO identifies flavors that are well
liked, i.e., high liking score, and consistently-liked,
i.e., of maximum confidence. Further, we generate
flavors that are well and consistently liked by a
cluster of panelists, by giving the MOPSO slightly
different objectives.",
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notes = "Also known as \cite{1830713} GECCO-2010 A joint
meeting of the nineteenth international conference on
genetic algorithms (ICGA-2010) and the fifteenth annual
genetic programming conference (GP-2010)",
- }
Genetic Programming entries for
Kalyan Veeramachaneni
Ekaterina (Katya) Vladislavleva
Matt Burland
Jason Parcon
Una-May O'Reilly
Citations