References
[1]
Kiyoharu Tagawa. 2012. Concurrent Differential Evolution Based on Generational Model for Multi-core CPUs. In Proceedings of the 9th International Conference on Simulated Evolution and Learning (SEAL'12). Springer-Verlag, Berlin, Heidelberg, 12--21.
[2]
Juan Julián Merelo Guervós, Juan Luis Jiménez Laredo, Pedro A. Castillo, José Mario García Valdez, Sergio Rojas Galeano:Exploring Concurrent and Stateless Evolutionary Algorithms. EvoApplications 2019: 405--412
[3]
José Mario García Valdez, Juan Julián Merelo Guervós: A modern, event-based architecture for distributed evolutionary algorithms. GECCO (Companion) 2018: 233--234
[4]
Juan Julián Merelo Guervós, José Mario García Valdez: Going Stateless in Concurrent Evolutionary Algorithms. WEA (1) 2018: 17--29
[5]
Gregory R Andrews. 1991. Concurrent programming: principles and practice. Benjamin/Cummings Publishing Company San Francisco.
[6]
John Hawkins and Ali Abdallah. 2001. A Generic Functional Genetic Algorithm. In Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications (AICCSA '01). IEEE Computer Society, Washington, DC, USA, 11--. http://dl.acm.org/citation.cfm?id=872017.872197
[7]
Kittisak Kerdprasop and Nittaya Kerdprasop. 2012. Concurrent Computation for Genetic Algorithms. In Proceedings of the 1st International Conference on Software Technology. 79--84.
[8]
J. L. J. Laredo, P.A. Castillo, A. M. Mora, and J. J. Merelo. 2008. Exploring population structures for locally concurrent and massively parallel Evolutionary Algorithms. In 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). 2605--2612.
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Published In
July 2019
2161 pages
ISBN:9781450367486
DOI:10.1145/3319619
- Editor:
- Manuel López-Ibáñez,
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- Anne Auger,
- Thomas Stützle
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Published: 13 July 2019
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