Evolving Nano Particle Cancer Treatments with Multiple Particle Types

Evolutionary algorithms have long been used for optimization problems where the appropriate size of solutions is unclear a priori. The applicability of this methodology is here investigated on the problem of designing a nano-particle (NP) based drug delivery system targeting cancer tumours. Utilizing a treatment comprising of multiple types of NPs is expected to be more effective due to the higher complexity of the treatment. This paper begins by utilizing the well-known NK model to explore the effects of fitness landscape ruggedness upon the evolution of genome length and, hence, solution complexity. The size of a novel sequence and the absence or presence of sequence deletion are also considered. Results show that whilst landscape ruggedness can alter the dynamics of the process, it does not hinder the evolution of genome length. These findings are then explored within the aforementioned real-world problem. In the first known instance, treatments with multiple types of NPs are used simultaneously, via an agent-based open source physics-based cell simulator. The results suggest that utilizing multiple types of NPs is more efficient when the solution space is explored with the evolutionary techniques under a predefined computational budget.