Evolutionary optimization of flavors

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Veeramachaneni:2010:gecco,
  author =       "Kalyan Veeramachaneni and Katya Vladislavleva and 
                 Matt Burland and Jason Parcon and Una-May O'Reilly",
  title =        "Evolutionary optimization of flavors",
  booktitle =    "GECCO '10: Proceedings of the 12th annual conference
                 on Genetic and evolutionary computation",
  year =         "2010",
  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",
  isbn13 =       "978-1-4503-0072-8",
  pages =        "1291--1298",
  keywords =     "genetic algorithms, genetic programming, Real world
                 applications",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830483.1830713",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  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.",
  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