A Genetic Decomposition Algorithm for Predicting Rainfall within Financial Weather Derivatives

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

  author =       "Sam Cramer and Michael Kampouridis and Alex Freitas",
  title =        "A Genetic Decomposition Algorithm for Predicting
                 Rainfall within Financial Weather Derivatives",
  booktitle =    "GECCO '16: Proceedings of the 2016 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich and Frank Neumann and 
                 Andrew M. Sutton and Martin Middendorf and Xiaodong Li and 
                 Emma Hart and Mengjie Zhang and Youhei Akimoto and 
                 Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and 
                 Daniele Loiacono and Julian Togelius and 
                 Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and 
                 Faustino Gomez and Carlos M. Fonseca and 
                 Heike Trautmann and Alberto Moraglio and William F. Punch and 
                 Krzysztof Krawiec and Zdenek Vasicek and 
                 Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and 
                 Boris Naujoks and Enrique Alba and Gabriela Ochoa and 
                 Simon Poulding and Dirk Sudholt and Timo Koetzing",
  pages =        "885--892",
  keywords =     "genetic algorithms, genetic programming",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4206-3",
  DOI =          "doi:10.1145/2908812.2908894",
  abstract =     "Regression problems provide some of the most
                 challenging research opportunities, where the
                 predictions of such domains are critical to a specific
                 application. Problem domains that exhibit large
                 variability and are of chaotic nature are the most
                 challenging to predict. Rainfall being a prime example,
                 as it exhibits very unique characteristics that do not
                 exist in other time series data. Moreover, rainfall is
                 essential for applications that surround financial
                 securities such as rainfall derivatives. This paper is
                 interested in creating a new methodology for increasing
                 the predictive accuracy of rainfall within the problem
                 domain of rainfall derivatives. Currently, the process
                 of predicting rainfall within rainfall derivatives is
                 dominated by statistical models, namely Markov-chain
                 extended with rainfall prediction (MCRP). In this
                 paper, we propose a novel algorithm for decomposing
                 rainfall, which is a hybrid Genetic Programming/Genetic
                 Algorithm (GP/GA) algorithm. Hence, the overall problem
                 becomes easier to solve. We compare the performance of
                 our hybrid GP/GA, against MCRP, Radial Basis Function
                 and GP without decomposition. We aim to show the
                 effectiveness that a decomposition algorithm can have
                 on the problem domain. Results show that in general
                 decomposition has a very positive effect by
                 statistically outperforming GP without decomposition
                 and MCRP.",
  notes =        "GECCO-2016 A Recombination of the 25th International
                 Conference on Genetic Algorithms (ICGA-2016) and the
                 21st Annual Genetic Programming Conference (GP-2016)",

Genetic Programming entries for Sam Cramer Michael Kampouridis Alex Alves Freitas