Fly algorithm: Difference between revisions

Revision as of 10:37, 10 April 2017

Revision as of 10:49, 10 April 2017

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where <math>input</math> is the input image, <math>x</math> and <math>y</math> are the pixel coordinates along the horizontal and vertical axis respectively, <math>W</math> and <math>H</math> are the image width and height in number of pixels respectively, <math>F</math> is the fly population, and <math>P</math> is a projection operator that creates an image from flies. This projection operator <math>P</math> can take many forms. In her work, Z. Ali Aboodd <ref name=Abbood2017EvoIASP /> uses [[OpenGL]] to generate different effects (e.g. mosaics, or spray paint). For speeding up the evaluation of the fitness functions, [[OpenCL]] is used too.

The algorithm starts with a population <math>F</math> that is randomly generated (see Line 3 in the algorithm above). [[File:initial_random_fly_populationInitial_random_fly_population.png|129x130px|thumb|right|Image of the initial population (random fly positions).]]<math>F</math> is then assessed using the global fitness to compute <math>G_{fitness}(F) = \overset{x<W}{\underset{y=0}{\sum}}\overset{j<H}{\underset{j=0}{\sum}}|input(x,y) - P[F](x,y)|</math> (see Line 10). <math>G_{fitness}</math> is an error metrics, it has to be minimised.

== See also ==