A. Lentzas, C. Nalmpantis, D. Vrakas (2019) Hyperparameter Tuning using Quantum Genetic Algorithms. In: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), Portland, OR, USA, 2019, pp. 1412-1416.
Correctly tuning the hyperparameters of a machine
learning model can improve classification results. Typically hyperparameter
tuning is made by humans and experience is
needed to fine tune them. Algorithmic approaches have been
extensively studied in the literature and can find better results.
In our work we employ a quantum genetic algorithm to address
the hyperparameter optimization problem. The algorithm is
based on qudits instead of qubits, allowing more available
states. Experiments were performed on two datasets MNIST
and CIFAR10 and results were compared against classic genetic
algorithms.