Abstract
There are applications where a binary image is given and a shape is to be reconstructed from it with some kind of evolutionary algorithms. A solution for this problem usually highly depends on the fitness function. On the one hand fitness function influences the convergence speed of the EA. On the other hand, fitness computation is done many times, therefore the fitness computation itself has to be reasonably fast. This paper tries to define what “reasonably fast” means, by giving a definition for the efficiency. A definition alone is however not enough, therefore several fitness functions and function classes are defined, and their efficiencies are examined.
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Ványi, R. (2002). Efficiently Computable Fitness Functions for Binary Image Evolution. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds) Applications of Evolutionary Computing. EvoWorkshops 2002. Lecture Notes in Computer Science, vol 2279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46004-7_28
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DOI: https://doi.org/10.1007/3-540-46004-7_28
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