Skip to main content
SpringerLink
Log in

A new parallel-by-cell approach to undistorted data compression based on cellular automaton and genetic algorithm

  • Regular Papers
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

In this paper, a new parallel-by-cell approach to the undistorted data compression based on cellular automaton and genetic algorithm is presented. The local compression rules in a cellular automaton are obtained by using a genetic evolutionary algorithm. The correctness of the hyper-parallel compression, the time complexity, and the relevant symbolic dynamic behaviour are discussed. In comparison with other traditional sequential or small-scale parallel methods for undistorted data compression, the proposed approach shows much higher real-time performance, better suitability and feasibility for the systolic hardware implementation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Menalie M. An Introduction to Genetic Algorithm. MIT Press, 1996.

  2. Bertoni A, Dorigo M. Implicit parallelism in genetic algorithm.Artificial Intelligence, 1993, 61(2): 307–314.

    Article  MATH  MathSciNet  Google Scholar 

  3. Beasley J E, Martin R R. A sequential niche technique for multimodal function optimization.Evolutionary Computation, 1993, 1(2): 101–125.

    Article  Google Scholar 

  4. Michalewicz Z. Genetic algorithm+data structure=evolution programs. University of North Carolina, Springer-Verlag, Berlin, Heidelberg, 1996.

    Google Scholar 

  5. Adler D. Genetic algorithm and simulated annealing: A marriage proposal. InProc. IEEE Int. Conference on Neural Networks, March 28–April 1, 1997, pp.1104–1109.

  6. Fonseca C M, Fleming P J. An overview of evolutionary algorithms in multiobjective optimization.Evolutionary Computation, 1995, 3(1): 165–180.

    Article  Google Scholar 

  7. Fogel D B. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, 1995.

  8. Rudolph G. Convergence properties of canonical genetic algorithms.IEEE Trans. Neural Networks, 1994, 5(1): 96–101.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Intelligence Technology and System, Tsinghua University, 100084, Beijing, P.R. China

    Gu Jing

  2. Department of Computer Science and Technology, East China University of Science and Technology, 200237, Shanghai, P.R. China

    Shuai Dianxun

Authors
  1. Gu Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuai Dianxun
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

Supported by the National Natural Science Foundation of China under grant No. 69773037 and the State Key Laboratory of Intelligence Technology and System, Tsinghua University.

GU Jing received her B.S. degree in 1998 from East China University of Science and Technology. She is currently an M.S. candidate in computer science and technology. Her research interests are distributed artificial intelligence, artificial life and embryo.

SHUAI Dianxun was born in 1941. He graduated from Center China University of Science and Technology in 1962 and received his Ph.D. degree in computer science and technology from Tsinghua University in 1986. He presently is a Professor and Ph.D. tutor in the Department of Computer Science and Engineering, East China University of Science and Technology. As a senior visiting scholar, he did research work in Tohoku University, Japan during 1980–1982, in Minnesota University and CDIC, USA in 1986–1987, and in Doshisha University and Kyoto Sangyo University, Japan in 1993–1997. His research interests are artificial intelligence, distributed parallel processing, computer architecture, genetic algorithm and multi-agent systems.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gu, J., Shuai, D. A new parallel-by-cell approach to undistorted data compression based on cellular automaton and genetic algorithm. J. Comput. Sci. & Technol. 14, 572–579 (1999). https://doi.org/10.1007/BF02951877

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02951877

Keywords

Search

Navigation