RT Journal Article
T1 Hybrid genetic algorithms in agent-based artificial market model for simulating fan tokens trading
A1 Alaminos Aguilera, David
A1 Salas-Compás, María Belén
A1 Fernández-Gámez, Manuel Ángel
K1 Criptografía
AB 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 technologyand decentralization, and they are constantly changing, and they have a high risk. Negotiation strategies musttherefore be suited to these new circumstances. The genetic algorithm offers a very appropriate approach toresolving these complex issues. However, very little is known about genetic algorithm methods in cryptographictokens. Accordingly, this paper presents a case study of the simulation of Fan Tokens trading by implementingselected best trading rule sets by a genetic algorithm that simulates a negotiation system through the Monte Carlomethod. We have applied Adaptive Boosting and Genetic Algorithms, Deep Learning Neural Network-GeneticAlgorithms, Adaptive Genetic Algorithms with Fuzzy Logic, and Quantum Genetic Algorithm techniques. Theperiod selected is from December 1, 2021 to August 25, 2022, and we have used data from the Fan Tokens ofParis Saint-Germain, Manchester City, and Barcelona, leaders in the market. Our results conclude that the Hybridand Quantum Genetic algorithm display a good execution during the training and testing period. Our study has amajor impact on the current decentralized markets and future business opportunities
PB Elsevier
YR 2024
FD 2024-01-04
LK https://hdl.handle.net/10630/32321
UL https://hdl.handle.net/10630/32321
LA eng
NO 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.
NO This research was funded by the Universitat de Barcelona, under the grant UB-AE-AS017634.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026