New genetic operators in the Fly algorithm: application to medical PET image reconstruction


This paper presents an evolutionary approach for image reconstruction in positron emission tomography (PET). Our reconstruction method is based on a cooperative coevolution strategy (also called Parisian evolution): the “fly algorithm”. Each fly is a 3D point that mimics a positron emitter. The flies’ position is progressively optimised using evolutionary computing to closely match the data measured by the imaging system. The performance of each fly is assessed using a “marginal evaluation” based on the positive or negative contribution of this fly to the performance of the population. Using this property, we propose a “thresholded-selection” method to replace the classical tournament method. A mitosis operator is also proposed. It is triggered to automatically increase the population size when the number of flies with negative fitness becomes too low.


F. P. Vidal, J. Louchet, J.-M. Rocchisani, and É. Lutton, “New genetic operators in the Fly algorithm: application to medical PET image reconstruction,” in Applications of Evolutionary Computation, Istanbul, Turkey, 2010, vol. 6024, pp. 292–301.


  author = {Vidal, F. P. and Louchet, J. and Rocchisani, {J.-M.} and Lutton, \'E.},
  title = {New genetic operators in the {Fly} algorithm: application to medical
      {PET} image reconstruction},
  booktitle = {Applications of Evolutionary Computation},
  year = {2010},
  series = {Lecture Notes in Computer Science},
  volume = {6024},
  pages = {292-301},
  month = apr,
  address = {Istanbul, Turkey},
  annotation = {Apr~7--9, 2010},
  note = {Nominated for best paper award},
  doi = {10.1007/978-3-642-12239-2_30},
  publisher = {Springer, Heidelberg}


   Doi: 10.1007/978-3-642-12239-2_30