Evolutionary Algorithms for Food Science and Technology

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Evelyne Lutton and Nathalie Perrot and Alberto Tonda",
  title =        "Evolutionary Algorithms for Food Science and
  publisher =    "Wiley",
  year =         "2016",
  volume =       "7",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-119-13683-5",
  URL =          "https://www.amazon.com/Evolutionary-Algorithms-Technology-Computer-Engineering/dp/1848218133",
  size =         "182 pages",
  abstract =     "Researchers and practitioners in food science and
                 technology routinely face several challenges, related
                 to sparseness and heterogeneity of data, as well as to
                 the uncertainty in the measurements and the
                 introduction of expert knowledge in the models.
                 Evolutionary algorithms (EAs), stochastic optimization
                 techniques loosely inspired by natural selection, can
                 be effectively used to tackle these issues. In this
                 book, we present a selection of case studies where EAs
                 are adopted in real-world food applications, ranging
                 from model learning to sensitivity analysis.",
  notes =        "Modelling expertise on Camembert cheese ripening.
                 Reviewed by \cite{Androutsopoulos:2019:GPEM}",

Genetic Programming entries for Evelyne Lutton Nathalie Perrot Alberto Tonda