Abstract
This work presents a novel approach to synthesize an artificial visual cortex based on what we call organic genetic programming. Primate brains have several distinctive features that help in the outstanding display of perception achieved by the visual system, including binocular vision, memory, learning, and recognition, to mention but a few. These features are processed by a complex arrangement of highly interconnected and numerous cortical visual areas. This paper describes a system composed of an artificial dorsal pathway, or where stream, and an artificial ventral pathway, or what stream, that are fused to create a kind of artificial visual cortex. The idea is to show that genetic programming is able to evolve a high number of heterogeneous trees thanks to the hierarchical structure of our virtual brain. Thus, the proposal uses two key ideas: 1) the recognition of objects can be achieved by a hierarchical structure using the concept of function composition, 2) the evolved functions can be related to the tissues of an artificial organ. Experimental results provide evidence that high recognition rates could be achieved for a well-known multiclass object recognition problem.
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Clemente, E., Olague, G., Dozal, L. (2013). Purposive Evolution for Object Recognition Using an Artificial Visual Cortex. In: Schütze, O., et al. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II. Advances in Intelligent Systems and Computing, vol 175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31519-0_23
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