  Working towards the development of an evolvable cancer treatment simulator,
the investigation of Differential Evolution was considered, motivated by the
high efficiency of variations of this technique in real-valued problems. A
basic DE algorithm, namely "DE/rand/1" was used to optimize the simulated
design of a targeted drug delivery system for tumor treatment on PhysiCell
simulator. The suggested approach proved to be more efficient than a standard
genetic algorithm, which was not able to escape local minima after a predefined
number of generations. The key attribute of DE that enables it to outperform
standard EAs, is the fact that it keeps the diversity of the population high,
throughout all the generations. This work will be incorporated with ongoing
research in a more wide applicability platform that will design, develop and
evaluate targeted drug delivery systems aiming cancer tumours.
