Accelerated optimisation of chemotherapy dose schedules using fitness inheritance

Cancer treatment by chemotherapy involves multiple applications of toxic drugs over a period of time. Optimising the schedule of these treatments can improve the outcome for the patient. A schedule of treatment and its effect on the tumour can be simulated by a mathematical growth model. However, when used in conjunction with a black-box optimisation algorithm such as an Evolutionary Algorithm (EA) to search for effective treatment schedules, the frequent use of the model can become computationally onerous. One approach to improve the efficiency of EAs is to use `fitness inheritance´, in which, for a proportion of candidate solutions, simple means are used to estimate the fitness, rather than use the computationally intensive model. We investigate two versions of fitness inheritance for the chemotherapy schedule optimisation problem, and demonstrate the significant improvement in efficiency that can be achieved. In particular, concerning the two main types of fitness inheritance (Averaged and Proportional), we find that the Averaged Inheritance strategy is highly effective in this case, and is strongly recommended for use in further investigations of chemotherapy optimisation using population-based search.