Optimisation of cancer chemotherapy schedules using directed intervention crossover approaches

Citation

Godley PM, Cowie J, Cairns D, McCall J & Howie C (2008) Optimisation of cancer chemotherapy schedules using directed intervention crossover approaches. In: IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE World Congress on Computational Intelligence. IEEE Congress on Evolutionary Computation 2008, CEC 2008, Hong Kong, 01.06.2008-06.06.2008. Piscataway, NJ: IEEE (Institute of Electrical and Electronics Engineers), pp. 2532-2537. http://ieeexplore.ieee.org/servlet/opac?punumber=4625778; https://doi.org/10.1109/CEC.2008.4631138

Abstract
This paper describes two directed intervention crossover approaches that are applied to the problem of deriving optimal cancer chemotherapy treatment schedules. Unlike traditional uniform crossover (UC), both the calculated expanding bin (CalEB) method and targeted intervention with stochastic selection (TInSSel) approaches actively choose an intervention level and spread based on the fitness of the parents selected for crossover. Our results indicate that these approaches lead to significant improvements over UC when applied to cancer chemotherapy scheduling.

Keywords
Genetic Algorithm; Chemotherapy; Crossover; Optimal Control