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Hands-on Artificial Evolution Through Brain Programming

Part of the Genetic and Evolutionary Computation book series (GEVO)

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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  • DOI: 10.1007/978-3-030-39958-0_12
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Notes

  1. 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. 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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Correspondence to Gustavo Olague .

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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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  • DOI: https://doi.org/10.1007/978-3-030-39958-0_12

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