A Single Population Genetic Programming based Ensemble Learning Approach to Job Shop Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Park:2015:GECCOcomp,

• author = "John Park and Su Nguyen and Mengjie Zhang and Mark Johnston",
• title = "A Single Population Genetic Programming based Ensemble Learning Approach to Job Shop Scheduling",
• booktitle = "GECCO Companion '15: Proceedings of the Companion Publication 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 = "978-1-4503-3488-4",
• keywords = "genetic algorithms, genetic programming: Poster",
• pages = "1451--1452",
• month = "11-15 " # jul,
• organisation = "SIGEVO",
• address = "Madrid, Spain",
• URL = "http://doi.acm.org/10.1145/2739482.2764651",
• DOI = "doi:10.1145/2739482.2764651",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "Genetic Programming based hyper-heuristics (GP-HH) for dynamic job shop scheduling (JSS) problems are approaches which aim to address the issue where heuristics are only effective for specific JSS problem domains, and that designing effective heuristics for JSS problems can be difficult. This paper is a preliminary investigation into improving the robustness of heuristics evolved by GP-HH by evolving ensembles of dispatching rules from a single population of GP individuals. The results show that the current approach does not evolve significantly better or more robust rules than a standard GP-HH approach of evolving single constituent rules.",
• notes = "Also known as \cite{2764651} Distributed at GECCO-2015.",

}

Genetic Programming entries for John Park Su Nguyen Mengjie Zhang Mark Johnston

Citations