Abstract
This paper is about the evolution of a bio-inspired methodologyhttp://evovision.cicese.mx that mimics the cortical visual pathways. The methodology has been extensively tested on problems with different levels of complexity with outstanding results. After a review of the main works, the problem of classification of digitized art is introduced. An image database of five classes downloaded from the Kaggle web site is used as a benchmark for evolutionary learning. A comparison with convolutional neural network from scratch and the well-known AlexNet is provided to illustrate the quality of the proposal in comparison with the state-of-the-art.
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Notes
- 1.
Nowadays, to claim that any computational method (artificial intelligence) is capable of solving similar visual problems needs to be taken carefully since the programs need to be explainable from the artistic viewpoint.
- 2.
Note that Koza classify neural networks as one of the existing methods that do not seek solutions in the form of computer programs.
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Acknowledgements
This research was partially funded by CICESE through the project 634-128, “Programación Cerebral Aplicada al Estudio del Pensamiento y la Visión”. The second author graciously acknowledges the scholarship paid by the National Council for Science and Technology of Mexico (CONACyT) under grant 25267-340078.
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Olague, G., Chan-Ley, M. (2020). Hands-on Artificial Evolution Through Brain Programming. In: Banzhaf, W., Goodman, E., Sheneman, L., Trujillo, L., Worzel, B. (eds) Genetic Programming Theory and Practice XVII. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-030-39958-0_12
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