Increasing the Throughput of Expensive Evaluations Through a Vector Based Genetic Programming Framework

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@InProceedings{Zutty:2016:GECCOcomp,
  author =       "Jason Zutty and Daniel Long and Gregory Rohling",
  title =        "Increasing the Throughput of Expensive Evaluations
                 Through a Vector Based Genetic Programming Framework",
  booktitle =    "GECCO 2016 Late-Breaking Abstracts",
  year =         "2016",
  editor =       "Francisco Chicano and 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 =        "1477--1478",
  keywords =     "genetic algorithms, genetic programming",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4323-7",
  DOI =          "doi:10.1145/2908961.2931641",
  abstract =     "traditional genetic programming only supports the use
                 of arithmetic and logical operators on scalar features.
                 The GTMOEP (Georgia Tech Multiple Objective
                 Evolutionary Programming) framework builds upon this by
                 also handling feature vectors, allowing the use of
                 signal processing and machine learning functions as
                 primitives, in addition to the more conventional
                 operators [6]. GTMOEP is a novel method for automated,
                 data-driven algorithm creation, capable of
                 outperforming human derived solutions.

                 A challenge in this field is working with both large
                 datasets and expensive primitive functions. This paper
                 outlines some of the innovations Zutty et al. have
                 introduced into the GTMOEP framework in order to more
                 efficiently evaluate individuals and tackle new
                 problems. These innovations include: Working with
                 non-feature data, tiered datasets, subtree caches, and
                 initial population creation.",
  notes =        "Distributed at GECCO-2016.",
}

Genetic Programming entries for Jason Zutty Daniel Long Gregory Rohling

Citations