A Memetic Algorithm for Multiple-Drug Cancer Chemotherapy Schedule Optimization

This correspondence introduces a multidrug cancer chemotherapy model to simulate the possible response of the tumor cells under drug administration. We formulate the model as an optimal control problem. The algorithm in this correspondence optimizes the multidrug cancer chemotherapy schedule. The ob...

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Veröffentlicht in:IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Jg. 37; H. 1; S. 84 - 91
Hauptverfasser: Tse, S.-M., Yong Liang, Kwong-Sak Leung, Kin-Hong Lee, Mok, T.S.-K.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.02.2007
Schlagworte:
Adaptive algorithms
Algorithms
Antineoplastic Combined Chemotherapy Protocols - administration & dosage
Antineoplastic Combined Chemotherapy Protocols - pharmacokinetics
Artificial Intelligence
Biomimetics - methods
Cancer
Chemotherapy
Computer Simulation
Cybernetics
Drug Administration Schedule
Drug scheduling (cancer) model
Drug Therapy, Computer-Assisted - methods
Drugs
evolutionary algorithms (EAs)
Genetic algorithms
Heuristic algorithms
Humans
hybrid genetic algorithm-local search (GA-LS)
Iterative algorithms
iterative dynamic programming (IDP)
memetic algorithms (MAs)
Models, Biological
Neoplasms
Neoplasms - drug therapy
Neoplasms - metabolism
Optimal control
Optimal scheduling
Optimization
Schedules
Scheduling algorithm
Software
Systems Theory
Tumors
ISSN:1083-4419
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Zusammenfassung:This correspondence introduces a multidrug cancer chemotherapy model to simulate the possible response of the tumor cells under drug administration. We formulate the model as an optimal control problem. The algorithm in this correspondence optimizes the multidrug cancer chemotherapy schedule. The objective is to minimize the tumor size under a set of constraints. We combine the adaptive elitist genetic algorithm with a local search algorithm called iterative dynamic programming (IDP) to form a new memetic algorithm (MA-IDP) for solving the problem. MA-IDP has been shown to be very efficient in solving the multidrug scheduling optimization problem
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ISSN:1083-4419
DOI:10.1109/TSMCB.2006.883265