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Reducing Alphabet Using Genetic Algorithms

  • Conference paper
Digital Information Processing and Communications (ICDIPC 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 189))

Abstract

In the past, several approaches for data compression were developed. The base approach use characters as basic compression unit, but syllable-based and word based approaches were also developed. These approaches define strict borders between basic units. These borders are valid only for tested collections. Moreover, there may be words, which are not syllables, but it is useful to use them even in syllable based approach or in character based approach. Of course, testing of all possibilities is not realizable in finite time. Therefor, a optimization technique may be used as possible solution. This paper describes first steps in the way to optimal compression alphabet - designing the basic algorithms for alphabet reduction using genetic algorithms.

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References

  1. Abramson, N.: Information Theory and Coding. McGraw-Hill, New York (1963)

    Google Scholar 

  2. Arnold, R., Bell, T.: A corpus for the evaluation of lossless compression algorithms. In: Storer, J.A., Cohn, M. (eds.) Proc. 1997 IEEE Data Compression Conference, pp. 201–210. IEEE Computer Society Press, Los Alamitos (1997)

    Google Scholar 

  3. Bentley, J.L., Sleator, D.D., Tarjan, R.E., Wei, V.K.: A locally adaptive data compression scheme. Commun. ACM 29(4), 320–330 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  4. Burrows, M., Wheeler, D.J.: A block-sorting lossless data compression algorithm. Technical report, Digital SRC Research Report (1994)

    Google Scholar 

  5. Cleary, J.G., Ian, Witten, H.: Data compression using adaptive coding and partial string matching. IEEE Transactions on Communications 32, 396–402 (1984)

    Article  Google Scholar 

  6. Dvorský, J., Pokorný, J., Snášel, V.: Word-based compression methods and indexing for text retrieval systems. In: Eder, J., Rozman, I., Welzer, T. (eds.) ADBIS 1999. LNCS, vol. 1691, pp. 75–84. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  7. Horspool, R.N.: Constructing word-based text compression algorithms. In: Proc. IEEE Data Compression Conference, pp. 62–81. IEEE Computer Society Press, Los Alamitos (1992)

    Google Scholar 

  8. Huffman, D.A.: A method for the construction of minimum-redundancy codes. Institute of Radio Engineers 40(9), 1098–1101 (1952)

    MATH  Google Scholar 

  9. Koza, J.: Genetic programming: A paradigm for genetically breeding populations of computer programs to solve problems. Technical Report STAN-CS-90-1314, Dept. of Computer Science, Stanford University (1990)

    Google Scholar 

  10. Kuthan, T., Lansky, J.: Genetic algorithms in syllable-based text compression. In: Pokorný, J., Snásel, V., Richta, K. (eds.) CEUR Workshop Proceedings. DATESO, vol. 235 (2007), CEUR-WS.org

  11. Lánský, J.: Slabiková komprese. Master’s thesis, Charles University in Prague, in czech language (April 2005)

    Google Scholar 

  12. Lansky, J., Chernik, K., Vlickova, Z.: Comparison of text models for bwt. In: DCC 2007: Proceedings of the 2007 Data Compression Conference, p. 389. IEEE Computer Society, Washington, DC, USA (2007)

    Google Scholar 

  13. Lansky, J., Zemlicka, M.: Text compression: Syllables. In: Richta, K., Snásel, V., Pokorný, J. (eds.) CEUR Workshop Proceedings. DATESO, vol. 129, pp. 32–45 (2005), CEUR-WS.org

  14. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)

    MATH  Google Scholar 

  15. Moffat, A.: Implementing the ppm data compression scheme. IEEE Transactions on Communications 38(11), 1917–1921 (1990)

    Article  Google Scholar 

  16. Moffat, A., Isal, R.Y.K.: Word-based text compression using the burrows-wheeler transform. Inf. Process. Manage. 41(5), 1175–1192 (2005)

    Article  MATH  Google Scholar 

  17. Rissanen, J.: Generalized kraft inequality and arithmetic coding. IBM Journal of Research and Development 20(3), 198–203 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  18. Rissanen, J., Langgon Jr, G.G.: Arithmetic coding. IBM Journal of Research and Development 23(2), 149–162 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  19. Salomon, D.: Data Compression - The Complete Reference, 4th edn. Springer, London (2007)

    MATH  Google Scholar 

  20. Shannon, C.E.: A mathematical theory of communication. Bell System Technical Journal 27, 379–423, 623–656 (1948)

    Article  MathSciNet  MATH  Google Scholar 

  21. Shannon, C.E.: Prediction and entropy of printed english. Bell Systems Technical Journal 30, 50–64 (1951)

    Article  MATH  Google Scholar 

  22. Üçoluk, G., Toroslu, I.H.: A genetic algorithm approach for verification of the syllable-based text compression technique. Journal of Information Science 23(5), 365–372 (1997)

    Article  Google Scholar 

  23. Witten, I., Moffat, A., Bell, T.: Managing Gigabytes: Compressing and Indexing Documents and Images. Van Nostrand Reinhold (1994)

    Google Scholar 

  24. Ziv, J., Lempel, A.: A universal algorithm for sequential data compression. IEEE Transactions on Information Theory IT-23(3), 337–343 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  25. Ziv, J., Lempel, A.: Compression of individual sequences via variable-rate coding. IEEE Transactions on Information Theory IT-24(5), 530–536 (1978)

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, FEECS, VSB-Technical University of Ostrava, 17. listopadu 15, 70833, Ostrava, Poruba, Czech Republic

    Jan Platos & Pavel Kromer

Authors
  1. Jan Platos
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  2. Pavel Kromer
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Editor information

Editors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, VÅ B-TUO, 17. listopadu 15, 708 33, Ostrava, Poruba, Czech Republic

    Vaclav Snasel  & Jan Platos  & 

  2. Information Systems Department, King Saud University, 11543, Riyadh, Saudi Arabia

    Eyas El-Qawasmeh

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Platos, J., Kromer, P. (2011). Reducing Alphabet Using Genetic Algorithms. In: Snasel, V., Platos, J., El-Qawasmeh, E. (eds) Digital Information Processing and Communications. ICDIPC 2011. Communications in Computer and Information Science, vol 189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22410-2_7

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  • DOI: https://doi.org/10.1007/978-3-642-22410-2_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22409-6

  • Online ISBN: 978-3-642-22410-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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