2026-05-30T00:09:07Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/323212026-02-03T10:55:43Zcom_10630_2254col_10630_37953

Hybrid genetic algorithms in agent-based artificial market model for simulating fan tokens trading Alaminos Aguilera, David Salas-Compás, María Belén Fernández-Gámez, Manuel Ángel Criptografía In recent years cryptographic tokens have gained popularity as they can be used as a form of emerging alter- native financing and as a means of building platforms. The token markets innovate quickly through technology and decentralization, and they are constantly changing, and they have a high risk. Negotiation strategies must therefore be suited to these new circumstances. The genetic algorithm offers a very appropriate approach to resolving these complex issues. However, very little is known about genetic algorithm methods in cryptographic tokens. Accordingly, this paper presents a case study of the simulation of Fan Tokens trading by implementing selected best trading rule sets by a genetic algorithm that simulates a negotiation system through the Monte Carlo method. We have applied Adaptive Boosting and Genetic Algorithms, Deep Learning Neural Network-Genetic Algorithms, Adaptive Genetic Algorithms with Fuzzy Logic, and Quantum Genetic Algorithm techniques. The period selected is from December 1, 2021 to August 25, 2022, and we have used data from the Fan Tokens of Paris Saint-Germain, Manchester City, and Barcelona, leaders in the market. Our results conclude that the Hybrid and Quantum Genetic algorithm display a good execution during the training and testing period. Our study has a major impact on the current decentralized markets and future business opportunities 2024-07-26T09:16:03Z 2024-07-26T09:16:03Z 2024-01-04 journal article Alaminos, D., Salas, M. B., & Fernández-Gámez, M. Á. (2024). Hybrid genetic algorithms in agent-based artificial market model for simulating fan tokens trading. Engineering Applications of Artificial Intelligence, 131, 107713. https://hdl.handle.net/10630/32321 10.1016/j.engappai.2023.107713 eng open access Elsevier