Advertisement

Evolutionary Computation: from Genetic Algorithms to Genetic Programming

  • Ajith Abraham
  • Nadia Nedjah
  • Luiza de Macedo Mourelle
Part of the Studies in Computational Intelligence book series (SCI, volume 13)

1.7 Summary

This chapter presented the biological motivation and fundamental aspects of evolutionary algorithms and its constituents, namely genetic algorithm, evolution strategies, evolutionary programming and genetic programming. Most popular variants of genetic programming are introduced. Important advantages of evolutionary computation while compared to classical optimization techniques are also discussed.

Keywords

Genetic Algorithm Evolutionary Algorithm Genetic Program Evolutionary Computation Travelling Salesman Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abraham, A., Evolutionary Computation, In: Handbook for Measurement, Systems Design, Peter Sydenham and Richard Thorn (Eds.), John Wiley and Sons Ltd., London, ISBN 0-470-02143-8, pp. 920–931, 2005.Google Scholar
  2. 2.
    Bäck, T., Evolutionary algorithms in theory and practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford University Press, New York, 1996.Google Scholar
  3. 3.
    Banzhaf, W., Nordin, P., Keller, E. R., Francone, F. D., Genetic Programming: An Introduction on The Automatic Evolution of Computer Programs and its Applications, Morgan Kaufmann Publishers, Inc., 1998.Google Scholar
  4. 4.
    Ferreira, C., Gene Expression Programming: A new adaptive algorithm for solving problems-Complex Systems, Vol. 13, No. 2, pp. 87–129, 2001.Google Scholar
  5. 5.
    Fogel, L.J., Owens, A.J. and Walsh, M.J., Artificial Intelligence Through Simulated Evolution, John Wiley & Sons Inc. USA, 1966.Google Scholar
  6. 6.
    Fogel, D. B. (1999) Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway, NJ, Second edition, 1999.Google Scholar
  7. 7.
    Goldberg, D. E., Genetic Algorithms in search, optimization, and machine learning, Reading: Addison-Wesley Publishing Corporation Inc., 1989.Google Scholar
  8. 8.
    History of Lisp, http://www-formal.stanford.edu/jmc/history/lisp.html, 2004.Google Scholar
  9. 9.
    Holland, J. Adaptation in Natural and Artificial Systems, Ann Harbor: University of Michican Press, 1975.Google Scholar
  10. 10.
    Jang, J.S.R., Sun, C.T. and Mizutani, E., Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall Inc, USA, 1997.Google Scholar
  11. 11.
    Koza. J. R., Genetic Programming. The MIT Press, Cambridge, Massachusetts, 1992.Google Scholar
  12. 12.
    Michalewicz, Z., Genetic Algorithms + Data Structures = Evolution Programs, Berlin: Springer-Verlag, 1992.Google Scholar
  13. 13.
    Miller, J. F. Thomson, P., Cartesian Genetic Programming, Proceedings of the European Conference on Genetic Programming, Lecture Notes In Computer Science, Vol. 1802 pp. 121–132, 2000.Google Scholar
  14. 14.
    Oltean M. and Grosan C., Evolving Evolutionary Algorithms using Multi Expression Programming. Proceedings of The 7th. European Conference on Artificial Life, Dortmund, Germany, pp. 651–658, 2003.Google Scholar
  15. 15.
    Oltean, M., Solving Even-Parity Problems using Traceless Genetic Programming, IEEE Congress on Evolutionary Computation, Portland, G. Greenwood, et. al (Eds.), IEEE Press, pp. 1813–1819, 2004.Google Scholar
  16. 16.
    Paterson, N. R. and Livesey, M., Distinguishing Genotype and Phenotype in Genetic Programming, Late Breaking Papers at the Genetic Programming 1996, J. R. Koza (Ed.), pp. 141–150,1996.Google Scholar
  17. 17.
    Rechenberg, I., Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution, Stuttgart: Fromman-Holzboog, 1973.Google Scholar
  18. 18.
    Ryan, C., Collins, J. J. and O’Neill, M., Grammatical Evolution: Evolving Programs for an Arbitrary Language, Proceedings of the First European Workshop on Genetic Programming (EuroGP’98), Lecture Notes in Computer Science 1391, pp. 83–95, 1998.Google Scholar
  19. 19.
    Schwefel, H.P., Numerische Optimierung von Computermodellen mittels der Evolutionsstrategie, Basel: Birkhaeuser, 1977.Google Scholar
  20. 20.
    Törn A. and Zilinskas A., Global Optimization, Lecture Notes in Computer Science, Vol. 350, Springer-Verlag, Berlin, 1989.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ajith Abraham
    • 1
  • Nadia Nedjah
    • 2
  • Luiza de Macedo Mourelle
    • 3
  1. 1.School of Computer Science and Engineering Chung-Ang University 410SeoulKorea
  2. 2.Department of Electronics Engineering and Telecommunications, Engineering FacultyState University of Rio de JaneiroSala Maracanã, Rio de JaneiroBrazil
  3. 3.Department of System Engineering and Computation, Engineering FacultyState University of Rio de JaneiroSala Maracanã, Rio de JaneiroBrazil

Personalised recommendations