Novelty search employed into the development of cancer treatment simulations
This work addresses optimization challenges in cancer treatment simulations, but it is incremental as it applies an existing novelty search method to a new domain.
The study tackled the problem of local optima in optimization by applying novelty search to design a targeted drug delivery system for cancer treatment using the PhysiCell simulator, resulting in an investigation of different weights in a hybrid objective equation to balance treatment effectiveness and solution novelty.
Conventional optimization methodologies may be hindered when the automated search is stuck into local optima because of a deceptive objective function landscape. Consequently, open ended search methodologies, such as novelty search, have been proposed to tackle this issue. Overlooking the objective, while putting pressure into discovering novel solutions may lead to better solutions in practical problems. Novelty search was employed here to optimize the simulated design of a targeted drug delivery system for tumor treatment under the PhysiCell simulator. A hybrid objective equation was used containing both the actual objective of an effective tumour treatment and the novelty measure of the possible solutions. Different weights of the two components of the hybrid equation were investigated to unveil the significance of each one.