Combining Parallel Genetic Algorithms and Machine Learning to Improve the Research of Optimal Vaccination Protocols

The developing of novel prophylactic and therapeutic vaccine candidates in the field of cancer immunology brought to very promising results against tumors, entitling full protection with reduced amount of the typical side effects of the actual conventional treatments. However, such treatments required a constant, life-long, administration procedure to keep protection. As both the period of protection and the relative number of administrations grow, the problem of finding the best administration protocol, in time and dosage, becomes more and more complex. Such a problem cannot be usually solved in in vivo experiments, as the costs in terms of time, money, and people would be prohibitive. We propose a hybrid approach that integrates machine learning and parallel genetic algorithms to enhance the research in silico of optimal administration protocols for a cancer vaccine. A neural network is used to improve both crossover and mutation operators. Preliminary results suggest that the use of such could bring to better administration protocols using a similar computational effort.