AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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Authors: ALINA ENESCU.
DOI: 10.24193/subbi.2020.1.06
Published Online: 2020-06-30
Published Print: 2020-06-30
pp. 75-90
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Cellular Automata (CA) are simple, easily parallelizable models that have been used extensively for various computational tasks. Such models are especially useful for image processing, as mapping automaton cells to image pixels is straightforward and intuitive. This paper proposes a novel optimization framework for CA rules based on evolutionary algorithms and used in edge detection. This approach addresses the problem of optimizing an individual CA rule for one image and extends it to the optimization of a generic CA rule for a set of similar images. In order to maximize the transferability of the optimized rule, the algorithm is trained on sets of images using a curriculum learning approach. A study of the supervised fitness function, as well as batch optimization experiments, show that the algorithm is robust and competitive with the state-of-the-art methods.
Keywords and phrases. Cellular Automata, Genetic Algorithm, Edge Detection, Curriculum Learning, Optimization.
2010 Mathematics Subject Classification. 65K10, 90C70.
