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Utilizing Differential Evolution into Optimizing Targeted Cancer Treatments

2021; Springer Nature; Linguagem: Inglês

10.1007/978-3-030-76928-4_17

2194-7287

Michail‐Antisthenis Tsompanas, Larry Bull, Andrew Adamatzky, Igor Balaž,

Cancer Genomics and Diagnostics

Working towards the development of an evolvable cancer treatment simulator, the investigation of including evolutionary optimization methods was considered. Namely, Differential Evolution (DE) is studied here, 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 in silico the design of a targeted drug delivery system (DDS) for tumor treatment on PhysiCell simulator. The suggested approach proved to be more efficient than a standard Genetic Algorithm (GA), 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 GAs, 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 DDSs aiming cancer tumours.