Applying Evolutionary Algorithms for Service Function Chaining in 5G Networks Mora García, Antonio Miguel Victoria-Mohammed, J. Medina Medina, Nuria Valenzuela Valdes, Juan Francisco 5G Software-Defined Networks Genetic Algorithm Service Function Chaining Route Optimization This work has been partially funded by projects TED2021-131699B-I00 and TED2021-129938B-I00 MICIU/AEI/10.13039/501100011033/ and by the European Union NextGenerationEU/PRTR. It has also been funded by projects PID2020-113462RB-I00 (ANIMALICOS) and PID2020-115570GB-C22 (DemocratAI::UGR) of the Spanish Ministry of Economy and Competitiveness; as well as project C-ING-179-UGR23 financed by the "Consejería de Universidades, Investigación e Innovación" (Andalusian Government, FEDER Program 2021-2027). The recent boom of 5G networks, coupled with the escalating demand among users for higher-capacity connections, is presenting a formidable challenge. 5G networks encompass a diverse array of advantageous technologies, prominently including Software- Defined Networking (SDN) together with Network Function Virtualization (NFV), in which a key problem is the optimal development of Service Function Chaining (SFC). This paper presents two approaches based on Genetic Algorithms (GAs), for the optimal composition of SFCs focused on network pathways, and tailored for the service chain required in different 5G connections (user service requests). The approaches have been tested on different instances of the problem with topologies including 6, 19 and 52 nodes. The obtained results are promising, showing optimal paths inside simple and complex topologies for every connection as well as for multiple connections. 2024-10-29T13:03:12Z 2024-10-29T13:03:12Z 2024-08-08 conference output Published version: A. M. Mora, J. Victoria-Mohammed, N. Medina-Medina and J. F. Valenzuela-Valdés, "Applying Evolutionary Algorithms for Service Function Chaining in 5G Networks," 2024 IEEE Congress on Evolutionary Computation (CEC), Yokohama, Japan, 2024, pp. 1-8, DOI: 10.1109/CEC60901.2024.10611819 https://hdl.handle.net/10481/96472 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional IEEE