- 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 = "
http://doi.acm.org/10.1145/2739480.2754694",
-
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)",
