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Soft Computing in Engineering Design and Manufacturing pp 13–22Cite as

Fitness Causes Bloat

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Abstract

The problem of evolving an artificial ant to follow the Santa Fe trail is used to study the well known genetic programming feature of growth in solution length. Known variously as “bloat”, “fluff” and increasing “structural complexity”, this is often described in terms of increasing “redundancy” in the code caused by “introns”.

Comparison between runs with and without fitness selection pressure, backed by Price’s Theorem, shows the tendency for solutions to grow in size is caused by fitness based selection. We argue that such growth is inherent in using a fixed evaluation function with a discrete but variable length representation. With simple static evaluation search converges to mainly finding trial solutions with the same fitness as existing trial solutions. In general variable length allows many more long representations of a given solution than short ones. Thus in search (without a length bias) we expect longer representations to occur more often and so representation length to tend to increase. That is fitness based selection leads to bloat.

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References

  1. Walter Alden Tackett, 1993, Genetic programming for feature discovery and image discrimination, Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93, University of Illinois at Urbana-Champaign, July, pp. 303-309.

    Google Scholar 

  2. Walter Alden Tackett, 1994, Recombination, Selection, and the Genetic Construction of Computer Programs, PhD thesis, University of Southern California, USA.

    Google Scholar 

  3. Peter John Angeline, 1994, Genetic programming and emergent intelligence, Advances in Genetic Programming, chapter 4, pp. 75-98, MIT Press, Cambridge, MA, USA.

    Google Scholar 

  4. Walter Alden Tackett, 1995, Greedy recombination and genetic search on the space of computer programs, Foundations of Genetic Algorithms 3, Estes Park, Colorado, USA, 31 July-2 August, pp. 271-297.

    Google Scholar 

  5. W. B. Langdon, 1995, Evolving data structures using genetic programming, Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95), Pittsburgh, PA, USA, July, pp. 295-302.

    Google Scholar 

  6. Peter Nordin and Wolfgang Banzhaf, 1995, Complexity compression and evolution, Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95), Pittsburgh, PA, USA, July, pp. 310-317.

    Google Scholar 

  7. Terence Soule, James A. Foster, and John Dickinson, 1996, Code growth in genetic programming, Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA, USA, July, pp. 215-223.

    Google Scholar 

  8. Annie S. Wu and Robert K. Lindsay, 1996, A survey of intron research in genetics, Parallel Problem Solving From Nature IV. Proceedings of the International Conference on Evolutionary Computation, Berlin, Germany, September, pp. 101-110.

    Google Scholar 

  9. Tobias Blickle and Lothar Thiele, 1994, Genetic programming and redundancy, Genetic Algorithms within the Framework of Evolutionary Computation (Workshop at KI-94, Saarbrücken), Im Stadtwald, Building 44, D-66123 Saarbrücken, Germany, pp. 33-38, Max-Planck-Institut für Informatik (MPI-I-94-241).

    Google Scholar 

  10. Tobias Blickte, 1996, Theory of Evolutionary Algorithms and Application to System Synthesis, PhD thesis, Swiss Federal Institute of Technology, Zurich.

    Google Scholar 

  11. Peter Nordin, Frank Francone, and Wolfgang Banzhaf, 1996, Explicitly defined introns and destructive crossover in genetic programming, Advances in Genetic Programming 2, chapter 6, pp. 111–134, MIT Press, Cambridge, MA, USA.

    Google Scholar 

  12. Nicholas Freitag McPhee and Justin Darwin Miller, 1995, Accurate replication in genetic programming. Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95), Pittsburgh, PA, USA, July, pp. 303-309.

    Google Scholar 

  13. Justinian P. Rosca and Dana H. Ballard, 1996, Complexity drift in evolutionary computation with tree representations, Technical Report NRL5, University of Rochester, Computer Science Department, Rochester, NY, USA.

    Google Scholar 

  14. W. B. Langdon, 1996, Data Structures and Genetic Programming, PhD thesis, University College, London.

    Google Scholar 

  15. John R. Koza, 1992, Genetic Programming: On the Programming of Computers by Natural Selection, MIT Press, Cambridge, MA, USA.

    MATH  Google Scholar 

  16. John R. Koza, 1994, Genetic Programming II: Automatic Discovery of Reusable Programs, MIT Press, Cambridge MA, USA.

    MATH  Google Scholar 

  17. George R. Price, 1970, Selection and covariance, Nature, 227, 1:520–521.

    Article  Google Scholar 

  18. W. B. Langdon and R. Poli, 1997, An analysis of the MAX problem in genetic programming, Genetic Programming 1997: Proceedings of the Second Annual Conference, Stanford University, CA, USA, July.

    Google Scholar 

  19. Lee Altenberg, 1995, The Schema Theorem and Price’s Theorem, Foundations of Genetic Algorithms 3, Estes Park, Colorado, USA, 31 July-2 August, pp. 23-49.

    Google Scholar 

  20. Justinian P. Rosca and Dana H. Ballard, 1996, Discovery of subroutines in genetic programming, Advances in Genetic Programming 2, chapter 9, pp. 177–202, MIT Press, Cambridge, MA, USA.

    Google Scholar 

  21. Chris Gathercole and Peter Ross, 1994, Dynamic training subset selection for supervised learning in genetic programming, Parallel Problem Solving from Nature III, Jerusalem, October, pp. 312-321.

    Google Scholar 

  22. Chris Gathercole and Peter Ross, 1996, An adverse interaction between crossover and restricted tree depth in genetic programming, Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA, USA, July, pp. 291-296.

    Google Scholar 

  23. Byoung-Tak Zhang and Heinz Mühlenbein, 1993, Evolving optimal neural networks using genetic algorithms with Occam’s razor, Complex Systems, 7, 199–220.

    Google Scholar 

  24. Hitoshi Iba, Hugo de Garis, and Taisuke Sato, 1994, Genetic programming using a minimum description length principle, Advances in Genetic Programming, chapter 12, pp. 265–284, MIT Press, Cambridge, MA, USA.

    Google Scholar 

  25. William B. Langdon, 1996, Data structures and genetic programming, Advances in Genetic Programming 2, chapter 20, pp. 395–414, MIT Press, Cambridge, MA, USA.

    Google Scholar 

  26. K. Sims, 1993, Interactive evolution of equations for procedural models, The Visual Computer, 9, 466–476.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science, The University of Birmingham, Birmingham, B15 2TT, UK

    W. B. Langdon & R. Poli

Authors
  1. W. B. Langdon
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  2. R. Poli
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Editor information

Editors and Affiliations

  1. School of Mechanical Engineering, University of Bath, Bath, BA2 7AY, UK

    P. K. Chawdhry Ph.D.

  2. The CIM Institute, Cranfield University, Cranfield, Bedford, MK43 0AL, UK

    R. Roy Ph.D.

  3. Department of Aerospace Engineering, Indian Institute of Technology, Powai, Mumbai, 400 076, India

    R. K. Pant PhD

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© 1998 Springer-Verlag London

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Langdon, W.B., Poli, R. (1998). Fitness Causes Bloat. In: Chawdhry, P.K., Roy, R., Pant, R.K. (eds) Soft Computing in Engineering Design and Manufacturing. Springer, London. https://doi.org/10.1007/978-1-4471-0427-8_2

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  • DOI: https://doi.org/10.1007/978-1-4471-0427-8_2

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-76214-0

  • Online ISBN: 978-1-4471-0427-8

  • eBook Packages: Springer Book Archive

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