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Modularization by Multi-Run Frequency Driven Subtree Encapsulation

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Genetic Programming Theory and Practice

Part of the book series: Genetic Programming Series ((GPEM,volume 6))

Abstract

In tree-based Genetic Programming, subtrees which represent potentially useful sub-solutions can be encapsulated in order to protect them and aid their prolifer-ation throughout the population. This paper investigates implementing this as a multi-run method. A two-stage encapsulation scheme based on subtree survival and frequency is compared against Automatically Defined Functions in fixed and evolved architectures and standard Genetic Programming for solving a Parity problem.

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References

  • Ahluwalia M. and Bull, L. (1999). Coevolving Functions in Genetic Programming: Classification Using K-Nearest-Neighbor, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 1999), Orlando, Florida, pp. 947–953. Morgan Kaufmann.

    Google Scholar 

  • Altenberg, L. (1994). “The Evolution of Evolvability in Genetic Programming. ” In Advances in Genetic Programming, Kinnear, K. E. Jr. (Ed. ), pp. 47–74. The MIT Press.

    Google Scholar 

  • Angeline P. J. and Pollack J. B. (1994). Coevolving High-Level Representations. In Artificial Life III, C. G. Langton (Ed. ), pp55–71, Addison-Wesley.

    Google Scholar 

  • Ashlock D., 1997. GP-Automata for dividing the dollar. In Genetic Programming: Proceedings of the Second Annual Conference, Koza, John et al. (Eds. ), pp 18–26. Stanford University.

    Google Scholar 

  • Davidenko D. F. (1953). On a New Method of Numerical Solution of Systems of Nonlinear Equations. Dokl. Akad. Nauk SSSR (USSR Academy of Sciences report), 88, pp. 601–602.

    MathSciNet  MATH  Google Scholar 

  • De Bono, E. (1970). Lateral Thinking: A Textbook of Creativity. Penguin Books Ltd., England.

    Google Scholar 

  • Dessi A., Giani A. and Starita A. (1999). An Analysis of Automatic Subroutine Discovery in Ge-netic Programming. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 1999), Orlando, Florida, pp. 996–1001. Morgan Kaufmann.

    Google Scholar 

  • Goldberg, D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading MA.

    MATH  Google Scholar 

  • Halmos P. R., 1981. Does Mathematics Have Elements? The Mathematical Intelligencer 3: 147–153.

    Article  MathSciNet  Google Scholar 

  • Howard D. and Roberts S. C. (1999). A Staged Genetic Programming Strategy for Image Analysis. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 1999), pp. 1047–1052, Orlando, Florida, Morgan Kaufmann.

    Google Scholar 

  • Howard D., Roberts S. C. and Brankin R. (1999). Target Detection in SAR Imagery by Genetic Programming. Advances in Engineering Software 30: 303–311.

    Article  Google Scholar 

  • Howard D., Roberts S. C. and Ryan C. (2002). Machine Vision: Exploring Context with Genetic Programming. In Genetic and Evolutionary Computation Conference (GECCO 2002), pp. 756–763. Morgan Kaufmann.

    Google Scholar 

  • Howard D. and Benson K. (2003). Evolutionary Computation Method for Pattern Recognition of cis-acting Sites. Biosystems , special issue on Computational Intelligence and Bioinformatics (in press).

    Google Scholar 

  • Igel C. and Chellapilla K. (1999). Investigating the Influence of Depth and Degree of Genotypic Change on Fitness. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 1999), pp 1061–1068, Orlando, Florida. Morgan Kaufmann.

    Google Scholar 

  • Koza, John (1992). Genetic Programming: On the Programming of Computers by Natural Se-lection. MIT Press, Cambridge, MA, USA.

    MATH  Google Scholar 

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

    MATH  Google Scholar 

  • Koza, J. R., Andre, D., Bennett, F. H. III and Keane, M. (1999). Genetic Programming 3: Darwinian Invention and Problem Solving. Morgan Kaufman, San Francisco, CA, USA.

    Google Scholar 

  • Langdon W. B. and Poli R. (1998a). Fitness Causes Bloat: Mutation. In Proceedings of the First European Genetic Programming Conference (Euro GP 98), Lecture Notes in Computer Science (LNCS), Volume 1391, Banzhaf, W. et al. (Eds. ), pp 37–48. Springer.

    Google Scholar 

  • Luke S., (2003). Modification Point Depth and Genome Growth in Genetic Programming. Evolutionary Computation 11 (1): 67–106.

    Article  Google Scholar 

  • McPhee N. F. and Hopper N. J. (1999). Analysis of Genetic Diversity through Population History. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO J 999), Orlando, Florida, pp. 1112–1120. Morgan Kaufmann.

    Google Scholar 

  • Poli R. and Page J. (2000). Solving High-Order Boolean Parity Problems with Smooth Uniform Crossover, Sub-Machine Code GP and Demes. Genetic Programming and Evolvable Machines 1 (1/2): 37–56.

    Article  MATH  Google Scholar 

  • Roberts S. C., Howard D. and Koza J. R. (2001). Evolving Modules in GP by Subtree Encapsulation. In Proceedings of the 4th European Conference (EuroGP 2001), Lake Como, Italy, April 2001. Lecture Notes in Computer Science (LNCS) Volume 2038. Springer.

    Google Scholar 

  • Rosca J. P. and Ballard D. H. (1996b). Discovery of Subroutines in Genetic Programming. In Advances in Genetic Programming 2, P. J. Angeline and K. E. Kinnear Jr. (Eds. ). The MIT Press.

    Google Scholar 

  • Soule T. and Foster J. A. (1998). Removal Bias: a New Cause of Code Growth in Tree Based Evolutionary Programming. In IEEE International Conference on Evolutionary Computation, pp. 178–186, Anchorage, Alaska, IEEE Press.

    Google Scholar 

  • Streeter M. J., Keane M. A. and Koza J. R. (2002). Iterative Refinement of Computational Circuits using Genetic Programming. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002), pp. 877–884. Morgan Kaufmann Publishers.

    Google Scholar 

  • Wasserstrom E. (1973). Numerical Solutions by the Continuation Method. SIAM Review 15(1): 89–119.

    Article  MathSciNet  MATH  Google Scholar 

  • Wolpert D. H. and Macready W. G. (1997). No Free Lunch Theorems for Optimization. IEEE Transactions of Evolutionary Computation 1(1): 67–82.

    Article  Google Scholar 

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Howard, D. (2003). Modularization by Multi-Run Frequency Driven Subtree Encapsulation. In: Riolo, R., Worzel, B. (eds) Genetic Programming Theory and Practice. Genetic Programming Series, vol 6. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8983-3_10

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  • DOI: https://doi.org/10.1007/978-1-4419-8983-3_10

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-4747-7

  • Online ISBN: 978-1-4419-8983-3

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