- 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. Google ScholarDigital Library
- 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--412Google Scholar
- José Mario García Valdez, Juan Julián Merelo Guervós: A modern, event-based architecture for distributed evolutionary algorithms. GECCO (Companion) 2018: 233--234 Google ScholarDigital Library
- Juan Julián Merelo Guervós, José Mario García Valdez: Going Stateless in Concurrent Evolutionary Algorithms. WEA (1) 2018: 17--29Google Scholar
- Gregory R Andrews. 1991. Concurrent programming: principles and practice. Benjamin/Cummings Publishing Company San Francisco. Google ScholarDigital Library
- 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 Google ScholarDigital Library
- Kittisak Kerdprasop and Nittaya Kerdprasop. 2012. Concurrent Computation for Genetic Algorithms. In Proceedings of the 1st International Conference on Software Technology. 79--84.Google Scholar
- 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.Google Scholar
- Concurrency in evolutionary algorithms
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