  Despite recent improvements in computer vision, artificial visual systems'
design is still daunting since an explanation of visual computing algorithms
remains elusive. Salient object detection is one problem that is still open due
to the difficulty of understanding the brain's inner workings. Progress on this
research area follows the traditional path of hand-made designs using
neuroscience knowledge. In recent years two different approaches based on
genetic programming appear to enhance their technique. One follows the idea of
combining previous hand-made methods through genetic programming and fuzzy
logic. The other approach consists of improving the inner computational
structures of basic hand-made models through artificial evolution. This
research work proposes expanding the artificial dorsal stream using a recent
proposal to solve salient object detection problems. This approach uses the
benefits of the two main aspects of this research area: fixation prediction and
detection of salient objects. We decided to apply the fusion of visual saliency
and image segmentation algorithms as a template. The proposed methodology
discovers several critical structures in the template through artificial
evolution. We present results on a benchmark designed by experts with
outstanding results in comparison with the state-of-the-art.
