Multiple Objective Vector-Based Genetic Programming Using Human-Derived Primitives

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Jason Zutty and Daniel Long and Heyward Adams and 
                 Gisele Bennett and Christina Baxter",
  title =        "Multiple Objective Vector-Based Genetic Programming
                 Using Human-Derived Primitives",
  booktitle =    "GECCO '15: Proceedings of the 2015 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and Terence Soule and 
                 Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and 
                 Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and Oliver Schuetze and 
                 Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and 
                 Carola Doerr",
  isbn13 =       "978-1-4503-3472-3",
  pages =        "1127--1134",
  keywords =     "genetic algorithms, genetic programming",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "",
  DOI =          "doi:10.1145/2739480.2754694",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  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. GTMOEP is a novel method for automated,
                 data-driven algorithm creation, capable of
                 outperforming human derived solutions.

                 As an example, GTMOEP was applied to the problem of
                 predicting how long an emergency responder can remain
                 in a hazmat suit before the effects of heat stress
                 cause the user to become unsafe. An existing
                 third-party physics model was leveraged for predicting
                 core temperature from various situational parameters.
                 However, a sustained high heart rate also means that a
                 user is unsafe. To improve performance, GTMOEP was
                 evaluated to predict an expected pull time, computed
                 from both thresholds during human trials.

                 GTMOEP produced dominant solutions in multiple
                 objective space to the performance of predictions made
                 by the physics model alone, resulting in a safer
                 algorithm for emergency responders to determine
                 operating times in harsh environments. The program
                 generated by GTMOEP will be deployed to a mobile
                 application for their use.",
  notes =        "Also known as \cite{2754694} GECCO-2015 A joint
                 meeting of the twenty fourth international conference
                 on genetic algorithms (ICGA-2015) and the twentith
                 annual genetic programming conference (GP-2015)",

Genetic Programming entries for Jason Zutty Daniel Long Heyward Adams Gisele Bennett Christina Baxter