Genetic Programming Bibliography entries for Jose Carlos Ortiz-Bayliss
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Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
GP coauthors/coeditors:
Erick Lara-Cardenas,
Xavier Sanchez-Diaz,
Ivan Amaya,
Jorge M Cruz-Duarte,
Ender Ozcan,
Andrew J Parkes,
Hugo Terashima-Marin,
Alejandro Sosa-Ascencio,
Santiago Enrique Conant-Pablos,
Genetic Programming conference papers by Jose Carlos Ortiz-Bayliss
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Erick Lara-Cardenas and Xavier Sanchez-Diaz and Ivan Amaya and Jorge M. Cruz-Duarte and Jose Carlos Ortiz-Bayliss.
A Genetic Programming Framework for Heuristic Generation for the Job-Shop Scheduling Problem. In
Lourdes Martinez-Villase\~nor and Oscar Herrera-Alc\'antara and Hiram E. Ponce and F\'elix Castro-Espinoza editors,
Advances in Soft Computing - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Mexico City, Mexico, October 12-17, 2020, Proceedings, Part I, volume 12468, pages 284-295, 2020. Springer.
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Alejandro Sosa-Ascencio and Hugo Terashima-Marin and Jose Carlos Ortiz-Bayliss and Santiago Enrique Conant-Pablos.
Grammar-based Selection Hyper-heuristics for Solving Irregular Bin Packing Problems. In
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 editors,
GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation, pages 111-112, Denver, USA, 2016. ACM.
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Jose Carlos Ortiz-Bayliss and Ender Ozcan and Andrew J. Parkes and Hugo Terashima-Marin.
A genetic programming hyper-heuristic: Turning features into heuristics for constraint satisfaction. In
13th UK Workshop on Computational Intelligence (UKCI 2013), pages 183-190, Guildford, 2013. IEEE.
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