Smart problem solving environment for medical decision support
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Optimising cancer chemotherapy using an estimation of distribution algorithm and genetic algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
An Adaptive Multimeme Algorithm for Designing HIV Multidrug Therapies
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Modeling and optimization of combined cytostatic and cytotoxic cancer chemotherapy
Artificial Intelligence in Medicine
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A methodology using heuristic search methods is proposed for optimizing cancer chemotherapies with drugs acting on a specific phase of the cell cycle. Specifically, two evolutionary algorithms, and a simulated annealing method are considered. The methodology relies on an underlying mathematical model for tumor growth that includes cycle phase specificity, and multiple applications of a single cytotoxic agent. The goal is to determine effective protocols for administering the agent, so that the tumor is eradicated, while the immune system remains above a given threshold. Results confirm that modern heuristic methods are a good choice for optimizing complex systems. The three algorithms considered produced effective solutions, and provided drug schedules suitable for practice, although some methods excelled others in performance. A discussion of comparative results is presented.