Multiobjective genetic programming approach for a smooth modeling of the release kinetics of a pheromone dispenser

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{DBLP:conf/gecco/Alfaro-CidEMMFSP09,
  author =       "Eva Alfaro-Cid and Anna Esparcia-Alcazar and 
                 Pilar Moya and J. J. Merelo and Beatriu Femenia-Ferrer and 
                 Ken Sharman and Jaime Primo",
  title =        "Multiobjective genetic programming approach for a
                 smooth modeling of the release kinetics of a pheromone
                 dispenser",
  booktitle =    "GECCO-2009 Symbolic regression and modeling workshop
                 (SRM)",
  year =         "2009",
  editor =       "Anna I. Esparcia and Ying-ping Chen and 
                 Gabriela Ochoa and Ender Ozcan and Marc Schoenauer and Anne Auger and 
                 Hans-Georg Beyer and Nikolaus Hansen and 
                 Steffen Finck and Raymond Ros and Darrell Whitley and 
                 Garnett Wilson and Simon Harding and W. B. Langdon and 
                 Man Leung Wong and Laurence D. Merkle and Frank W. Moore and 
                 Sevan G. Ficici and William Rand and Rick Riolo and 
                 Nawwaf Kharma and William R. Buckley and Julian Miller and 
                 Kenneth Stanley and Jaume Bacardit and Will Browne and 
                 Jan Drugowitsch and Nicola Beume and Mike Preuss and 
                 Stephen L. Smith and Stefano Cagnoni and Jim DeLeo and 
                 Alexandru Floares and Aaron Baughman and 
                 Steven Gustafson and Maarten Keijzer and Arthur Kordon and 
                 Clare Bates Congdon and Laurence D. Merkle and 
                 Frank W. Moore",
  pages =        "2225--2230",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  doi =          "doi:10.1145/1570256.1570309",
  abstract =     "The accurate modeling of the release kinetics of
                 pheromone dispensers is a matter or great importance
                 for ensuring that the dispenser field-life covers the
                 flight period of the pest and for optimizing the layout
                 of dispensers in the treated area. A new experimental
                 dispenser has been recently designed by researchers at
                 the Instituto Agroforestal del Mediterraneo - Centro de
                 Ecologia Quimica Agricola (CEQA) of the Universidad
                 Politecnica de Valencia (Spain). The most challenging
                 problem for the modeling of the release kinetics of
                 this dispensers is the difficulty in obtaining
                 experimental measurements for building the model. The
                 procedure for obtaining these data is very costly, both
                 time and money wise, therefore the available data
                 across the whole season are scarce. In prior work we
                 demonstrated the utility of using Genetic Programming
                 (GP) for this particular problem. However, the models
                 evolved by the GP algorithm tend to have
                 discontinuities in those time ranges where there are
                 not available measurements. In this work we propose the
                 use of a multiobjective Genetic Programming for
                 modeling the performance of the CEQA dispenser. We take
                 two approaches, involving two and nine objectives
                 respectively. In the first one, one of the objectives
                 of the GP algorithm deals with how well the model fits
                 the experimental data, while the second objective
                 measures how {"}smooth{"} the model behaviour is. In
                 the second approach we have as many objectives as data
                 points and the aim is to predict each point separately
                 using the remaining ones. The results obtained endorse
                 the utility of this approach for those modeling
                 problems characterized by the lack of experimental
                 data.",
  notes =        "Distributed on CD-ROM at GECCO-2009.

                 ACM Order Number 910092.",
}

Genetic Programming entries for Eva Alfaro-Cid Anna Esparcia-Alcazar Pilar Moya Juan Julian Merelo Beatriu Femenia-Ferrer Kenneth C Sharman Jaime Primo