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

Evolving Nano Particle Cancer Treatments with Multiple Particle Types