Fig. 4: Machine learning module of the EVONANO platform.
From: Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment
We use (a) an evolutionary algorithm to optimise nanoparticle design. This optimisation routine first initialises a population of different parameter sets and evaluates the fitness of each parameter set using the tissue module. Parameter sets with high fitness are selected for (using a tournament procedure) and these sets are used to generate a new population of nanoparticle parameters using the crossover operator (where the information of the two parent solutions are combined to make new solutions), and the mutation operator. This process is repeated for a prescribed number of generations, resulting in an overall increase in the fitness of the parameters and effective nanoparticle treatments. We show in (b) the mean, maximum and minimum change in fitness across generations and highlight the changes of importance in parameters as we optimise the nanoparticle treatment, where parameter changes are shown in the table.
