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High quality offset printing: an evolutionary approach

Published: 07 July 2007 Publication History

Abstract

Print media are still very important for everyone's daily life. Current efforts are concerned with the application of the well-established offset-printing technology to other media, particularly cardboards, which require some substantial adaptations. To this end, this paper proposed a new specific pre-processing stage. This pre-processing stage can be configured by several parameters. This paper optimizes these parameter settings by using evolution strategies. It turns out that this optimization reduces the required energy and the number of wrongly generated pixels by about 15%, respectively.

References

[1]
D.B. Fogel. Evolutionary Computation: Toward a New Philosophy of Machine Learning Intelligence. IEEE Press, NJ, 1995.
[2]
L.J. Fogel, Autonomous Automata. Industrial Research, 4:14--19, 1962.
[3]
D.E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA, 1989.
[4]
I. Rechenberg, Evolutionsstrategie. Frommann-Holzboog, Stuttgart, 1994.
[5]
H.-P. Schwefel. Evolution and Optimum Seeking. John Wiley and Sons, NY. 1995.
[6]
R. Joost, R. Salomon. Hardware-Software Co-Design in Practice: A Case Study in Image Processing, In Proceedings of the 32nd Annual Conference of the IEEE Industrial Electronics Society (IECON), Paris, France, Nov. 2006.
[7]
D. Hubel. Eye, Brain, and Vision (Scientific American Library, No 22), W. H. Freeman, 1995.

Cited By

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  • (2023)A Study on DNN-Based Practical Model for Predicting Spot ColorApplied Sciences10.3390/app13241310013:24(13100)Online publication date: 8-Dec-2023
  • (2011)Advances in computational intelligence-based print quality assessment and control in offset colour printingExpert Systems with Applications: An International Journal10.1016/j.eswa.2011.04.03538:10(13441-13447)Online publication date: 15-Sep-2011

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cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2007

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Author Tags

  1. evolutionary algorithms
  2. image processing
  3. industrial application

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2023)A Study on DNN-Based Practical Model for Predicting Spot ColorApplied Sciences10.3390/app13241310013:24(13100)Online publication date: 8-Dec-2023
  • (2011)Advances in computational intelligence-based print quality assessment and control in offset colour printingExpert Systems with Applications: An International Journal10.1016/j.eswa.2011.04.03538:10(13441-13447)Online publication date: 15-Sep-2011

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