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A dynamical model of cancer chemotherapy with disturbance

Published: 07 July 2012 Publication History

Abstract

This work proposes a controlled stochastic difference equation model of scheduling, with quadratic cost criteria, for cancer chemotherapy. By reducing the problem to quadratic control optimization and introducing a random search algorithm, we seek an optimal chemotherapy schedule. Our ultimate goal is to provide more realistic solutions than previous models. To reach this goal, our model ideally kills the maximum number of cancer cells to eradicate the disease while preserving the number of normal cells. Our results show the proposed model works well for cancer chemotherapy. Our algorithm is fast and helps produce practical schedules.

References

[1]
A. Brownlee, M. Pelikan, J. McCall, and A. Petrovski. An application of a multivariate estimation of distribution algorithm to cancer chemotherapy. GECCO'08, pages 463-464, 2008.
[2]
R. Martin. Optimal control drug scheduling of cancer chemotherapy. Automatica, 28:1113-1123, 1992.
[3]
G. Ochoa, M. Villasana, and E. Burke. An evolutionary approach to cancer chemotherapy scheduling. Genetic Prog. and Evol. Machines Journal, 8(4):301-318, 2007.
[4]
Y. Sakamura. Cell concentration data, 2010. http://bsw3.naist.jp/tns/LabMembers/Research Fellow/Relative_Cell_Concentrations.pdf.

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Published In

cover image ACM Conferences
GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
July 2012
1586 pages
ISBN:9781450311786
DOI:10.1145/2330784

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2012

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Author Tags

  1. algorithm
  2. cancer model
  3. chemotherapy
  4. controlled stochastic difference equation
  5. nonlinear dynamics
  6. optimal control

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GECCO '12
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GECCO '12: Genetic and Evolutionary Computation Conference
July 7 - 11, 2012
Pennsylvania, Philadelphia, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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