Evolutionary Approximation of Edge Detection Circuits

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{Dvoracek:2016:EuroGP,

• author = "Petr Dvoracek and Lukas Sekanina",
• title = "Evolutionary Approximation of Edge Detection Circuits",
• booktitle = "EuroGP 2016: Proceedings of the 19th European Conference on Genetic Programming",
• year = "2016",
• month = "30 " # mar # "--1 " # apr,
• editor = "Malcolm I. Heywood and James McDermott and Mauro Castelli and Ernesto Costa and Kevin Sim",
• series = "LNCS",
• volume = "9594",
• publisher = "Springer Verlag",
• address = "Porto, Portugal",
• pages = "19--34",
• organisation = "EvoStar",
• keywords = "genetic algorithms, genetic programming, Cartesian genetic programming",
• isbn13 = "978-3-319-30668-1",
• DOI = "doi:10.1007/978-3-319-30668-1_2",
• abstract = "Approximate computing exploits the fact that many applications are inherently error resilient which means that some errors in their outputs can safely be exchanged for improving other parameters such as energy consumption or operation frequency. A new method based on evolutionary computing is proposed in this paper which enables to approximate edge detection circuits. Rather than evolving approximate edge detectors from scratch, key components of existing edge detector are replaced by their approximate versions obtained using Cartesian genetic programming (CGP). Various approximate edge detectors are then composed and their quality is evaluated using a database of images. The paper reports interesting edge detectors showing a good tradeoff between the quality of edge detection and implementation cost.",
• notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in conjunction with EvoCOP2016, EvoMusArt2016 and EvoApplications2016",

}

Genetic Programming entries for Petr Dvoracek Lukas Sekanina

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