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A genetic algorithm with neural network fitness function evaluation for IMRT beam angle optimization

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  • Joana Dias

    ()

  • Humberto Rocha
  • Brígida Ferreira
  • Maria Lopes

Abstract

Intensity Modulated Radiotherapy Treatment (IMRT) is a technique used in the treatment of cancer, where the radiation beams are modulated by a multileaf collimator allowing the irradiation of the patient using non-uniform radiation fields from selected angles. Beam angle optimization consists in trying to find the best set of angles that should be used in IMRT planning. The choice of this set of angles is patient and pathology dependent and, in clinical practice, most of the times it is made using a trial and error procedure or simply using equidistantly distributed angles. In this paper we propose a genetic algorithm that aims at calculating good sets of angles in an automated way, given a predetermined number of angles. We consider the discretization of all possible angles in the interval [0 $$^{\circ }$$ , 360 $$^{\circ }$$ ], and each individual is represented by a chromosome with 360 binary genes. As the calculation of a given individual’s fitness is very expensive in terms of computational time, the genetic algorithm uses a neural network as a surrogate model to calculate the fitness of most of the individuals in the population. To explicitly consider the estimation error that can result from the use of this surrogate model, the fitness of each individual is represented by an interval of values and not by a single crisp value. The genetic algorithm is capable of finding improved solutions, when compared to the usual equidistant solution applied in clinical practice. The genetic algorithm will be described and computational results will be shown. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Joana Dias & Humberto Rocha & Brígida Ferreira & Maria Lopes, 2014. "A genetic algorithm with neural network fitness function evaluation for IMRT beam angle optimization," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(3), pages 431-455, September.
  • Handle: RePEc:spr:cejnor:v:22:y:2014:i:3:p:431-455
    DOI: 10.1007/s10100-013-0289-4
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    File URL: http://hdl.handle.net/10.1007/s10100-013-0289-4
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    References listed on IDEAS

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    1. Lim, Gino J. & Cao, Wenhua, 2012. "A two-phase method for selecting IMRT treatment beam angles: Branch-and-Prune and local neighborhood search," European Journal of Operational Research, Elsevier, vol. 217(3), pages 609-618.
    2. Shanker, M. & Hu, M. Y. & Hung, M. S., 1996. "Effect of data standardization on neural network training," Omega, Elsevier, vol. 24(4), pages 385-397, August.
    3. J. Deasy & E. Lee & T. Bortfeld & M. Langer & K. Zakarian & J. Alaly & Y. Zhang & H. Liu & R. Mohan & R. Ahuja & A. Pollack & J. Purdy & R. Rardin, 2006. "A collaboratory for radiation therapy treatment planning optimization research," Annals of Operations Research, Springer, vol. 148(1), pages 55-63, November.
    4. H. Rocha & J. Dias & B. Ferreira & M. Lopes, 2013. "Selection of intensity modulated radiation therapy treatment beam directions using radial basis functions within a pattern search methods framework," Journal of Global Optimization, Springer, vol. 57(4), pages 1065-1089, December.
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    Cited by:

    1. Guillermo Cabrera-Guerrero & Andrew J. Mason & Andrea Raith & Matthias Ehrgott, 2018. "Pareto local search algorithms for the multi-objective beam angle optimisation problem," Journal of Heuristics, Springer, vol. 24(2), pages 205-238, April.
    2. Gerhard Weber & Jacek Blazewicz & Marion Rauner & Metin Türkay, 2014. "Recent advances in computational biology, bioinformatics, medicine, and healthcare by modern OR," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(3), pages 427-430, September.
    3. Aydin Azizi, 2017. "Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing," Complexity, Hindawi, vol. 2017, pages 1-18, June.

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