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
