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Genetic Programming with One-Point Crossover

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Book cover Soft Computing in Engineering Design and Manufacturing

Abstract

In recent theoretical and experimental work on schemata in genetic programming we have proposed a new simpler form of crossover in which the same crossover point is selected in both parent programs. We call this operator one-point crossover because of its similarity with the corresponding operator in genetic algorithms. One-point crossover presents very interesting properties from the theory point of view. In this paper we describe this form of crossover as well as a new variant called strict one-point crossover highlighting their useful theoretical and practical features. We also present experimental evidence which shows that one-point crossover compares favourably with standard crossover.

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References

  1. John R. Koza, 1992, Genetic Programming; On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, Massachusetts.

    MATH  Google Scholar 

  2. John R. Koza, 1994, Genetic Programming II: Automatic Discovery of Reusable Programs, MIT Press, Cambridge, Massachusetts.

    MATH  Google Scholar 

  3. K. E. Kinnear, Jr., Ed., 1994, Advances in Genetic Programming, MIT Press, Cambridge, Massachusetts.

    Google Scholar 

  4. John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, Eds., 1996, Genetic Programming 1996: Proceedings of the First Annual Conference, MIT Press, Cambridge, Massachusetts.

    Google Scholar 

  5. Peter J. Angeline and K. E. Kinnear, Jr., Eds., 1996, Advances in Genetic Programming 2, MIT Press, Cambridge, MA, USA.

    Google Scholar 

  6. Riccardo Poli, 1996, Genetic programming for image analysis, in Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA, USA, July, pp. 363-368.

    Google Scholar 

  7. Riccardo Poli and Stefano Cagnoni, 1997, Genetic Programming with User-Driven Selection: Experiments on the Evolution of Algorithms for Image Enhancement, in Genetic Programming 1997: Proceedings of the Second Annual Conference, Stanford University, CA, USA, July, pp. 269-276.

    Google Scholar 

  8. Riccardo Poli, 1997, Discovery of symbolic, neuro-symbolic and neural networks with parallel distributed genetic programming, in 3rd International Conference on Artificial Neural Networks and Genetic Algorithms, ICANNGA′97, Norwich, UK.

    Google Scholar 

  9. Riccardo Poli, 1997, Evolution of recursive transistion networks for natural language recognition with parallel distributed genetic programming, in Proceedings of AlSB-97 Workshop on Evolutionary Computation, Manchester, UK.

    Google Scholar 

  10. William B. Langdon, 1996, A bibliography for genetic programming, in Advances in Genetic Programming 2, Peter J. Angelinc and K. E. Kinnear, Jr., Eds., appendix B, pp. 507–532, MIT Press, Cambridge, MA, USA.

    Google Scholar 

  11. John Holland, 1992, Adaptation in Natural and Artificial Systems, MIT Press, Cambridge, Massachusetts, second edition.

    Google Scholar 

  12. David E. Goldberg, 1989, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, Massachusetts.

    MATH  Google Scholar 

  13. Una-May O’Reilly and Franz Oppacher, 1994, The troubling aspects of a building block hypothesis for genetic programming, in Foundations of Genetic Algorithms 3, Estes Park, Colorado, USA, July, pp. 73-88.

    Google Scholar 

  14. P. A. Whigham, 1995, A schema theorem for context-free grammars, in 1995 IEEE Conference on Evolutionary Computation, Perth, Australia, Nov, pp. 178-181.

    Google Scholar 

  15. Riccardo Poli and W. B. Langdon, 1997, A new schema theory for genetic programming with one-point crossover and point mutation, in Genetic Programming 1997: Proceedings of the Second Annual Conference, Stanford University, CA, USA, July, pp. 278-285.

    Google Scholar 

  16. Ben McKay, Mark J. Willis, and Geoffrey W. Barton, 1995, Using a tree structured genetic algorithm to perform symbolic regression, in First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA, Sheffield, UK, Sept, pp. 487-492.

    Google Scholar 

  17. Riccardo Poli and W. B. Langdon, 1997, An experimental analysis of schema creation, propagation and disruption in genetic programming, Technical Report CSRP-97-8, University of Birmingham, School of Computer Science, Feb., To be presented at ICGA-97.

    Google Scholar 

  18. Patrik D’haeseleer, 1994, Context preserving crossover in genetic programming, in Proceedings of the 1994 IEEE World Congress on Computational Intelligence, Orlando, Florida, USA, June, pp. 256-261.

    Google Scholar 

  19. W. B. Langdon and R. Poli, 1997, Fitness causes bloat, in Proceedings of the Second On-line World Conference on Soft Computing (WSC2), Internet, June, 1997.

    Google Scholar 

  20. N. N. Schraudolph and R. K. Belew, 1992, Dynamic parameter encoding for genetic algorithms, Machine Learning, 9, pp. 9–21.

    Google Scholar 

  21. Darrel Whitley, 1993, A genetic algorithm tutorial, Tech. Rep. CS-93-103, Department of Computer Science, Colorado State University.

    Google Scholar 

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© 1998 Springer-Verlag London

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Poli, R., Langdon, W.B. (1998). Genetic Programming with One-Point Crossover. 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_20

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  • DOI: https://doi.org/10.1007/978-1-4471-0427-8_20

  • 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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