Population-based optimization of cytostatic/cytotoxic combination cancer chemotherapy

Citation

Ochoa G & Villasana M (2013) Population-based optimization of cytostatic/cytotoxic combination cancer chemotherapy. Soft Computing, 17 (6), pp. 913-924. https://doi.org/10.1007/s00500-013-1043-5

Abstract
This article studies the suitability of modern population based algorithms for designing combination cancer chemotherapies. The problem of designing chemotherapy schedules is expressed as an optimization problem (an optimal control problem) where the objective is to minimize the tumor size without compromising the patient's health. Given the complexity of the underlying mathematical model describing the tumor's progression (considering two types of drugs, the cell cycle and the immune system response), analytical and classical optimization methods are not suitable, instead, stochastic heuristic optimization methods are the right tool to solve the optimal control problem. Considering several solution quality and performance metrics, we compared three powerful heuristic algorithms for real-parameter optimization, namely, CMA evolution strategy, differential evolution, and particle swarm pattern search method. The three algorithms were able to successfully solve the posed problem. However, differential evolution outperformed its counterparts both in quality of the obtained solutions and efficiency of search.

Keywords
Evolutionary algorithms; Differential evolution; Evolution strategies; Particle swarm optimization; Numerical optimization; Optimal control; Cancer chemotherapy; Combination chemotherapy

Journal
Soft Computing: Volume 17, Issue 6