Abstract
This paper describes an application of Parallel Distributed Genetic Programming (PDGP) to the problem of inducing recognisers for natural language from positive and negative examples. PDGP is a new form of Genetic Programming (GP) which is suitable for the development of programs with a high degree of parallelism and an efficient and effective reuse of partial results. Programs are represented in PDGP as graphs with nodes representing functions and terminals, and links representing the flow of control and results. PDGP allows the exploration of a large space of possible programs including standard tree-like programs, logic networks, neural networks, finite state automata, Recursive Transition Networks (RTNs), etc. The paper describes the representations, the operators and the interpreters used in PDGP, and describes how these can be tailored to evolve RTN-based recognisers.
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References
Late Breaking Papers at the Genetic Programming 1996 Conference, Stanford University, July 1996. Stanford Bookstore.
David Andre, Forrest H. Bennett III, and John R. Koza. Discovery by genetic programming of a cellular automata rule that is better than any known rule for the majority classification problem. In John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 3–11, Stanford University, CA, USA, 28–31 July 1996. MIT Press.
Forrest H. Bennett III. Automatic creation of an efficient multi-agent architecture using genetic programming with architecture-altering operations. In John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 30–38, Stanford University, CA, USA, 28–31 July 1996. MIT Press.
Scott Brave. Evolving deterministic finite automata using cellular encoding. In John R. Koza; David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 39–44, Stanford University, CA, USA, 28–31 July 1996. MIT Press.
Ted E. Dunning and Mark W. Davis.Evolutionary algorithms for natural language processing. In John R. Koza, editor, Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28–31, 1996, pages 16–23, Stanford University, CA, USA, 28–31 July 1996. Stanford Bookstore.
F Gruau and D. Whitley. Adding learning to the cellular development process: a comparative study. Evolutionary Computation, 1(3):213–233, 1993.
Frederic Gruau. Genetic micro programming of neural networks. In Kenneth E. Kinnear, Jr., editor, Advances in Genetic Programming, chapter 24, pages 495–518. MIT Press, 1994.
R. Jagannathan. Dataflow models. In E. Y. Zomaya, editor, Parallel and Distributed Computing Handbook. McGraw-Hill, 1995.
K. E. Kinnear, Jr., editor. Advances in Genetic Programming. MIT Press, 1994.
J. R. Koza, D. E. Goldberg, D. B. Fogel, and R. L. Riolo, editors. Proceedings of the First International Conference on Genetic Programming, Stenford University, July 1996. MIT Press.
John R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, 1992.
John R. Koza.Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Pres, Cambridge, Massachusetts, 1994.
John R. Koza, David Andre, Forrest H. Bennett III, and Martin A. Keane. Use of automatically defined functions and architecture-altering operations in automated circuit synthesis using genetic programming. In John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 132–149, Stanford University, CA, USA, 28–31 July 1996. MIT Press.
John R. Koza, Forest H. Bennett III, Jason Lohn, Frank Dunlap, Martin A. Keane, and David Andre. Use of architecture-altering operations to dynamically adapt a three-way analog source identification circuit to accommodate a new source. In John R. Koza, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max Garzon, Hitoshi Iba, and Rick L. Riolo, editors, Genetic Programming 1997: Proceedings of the Second Annual Conference, Stanford University, CA, USA, 13–16 July 1997. Morgan Kaufmann.
John R. Koza, Forrest H. Bennett III, David Andre, and Martin A. Keane. Automated WYWIWYG design of both the topology and component values of electrical circuits using genetic programming. In John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 123–131, Stanford University, CA, USA, 28–31 July 1996. MIT Press.
Thomas R. Osborn, Adib Charif, Ricardo Lamas, and Eugene Dubossarsky. Genetic logic programming. In 1995 IEEE Conference on Evolutionary Computation, volume 2, page 728, Perth, Australia, 29 November–1 December 1995. IEEE Press.
R. Poli. Parallel distributed genetic programming. Technical Report CSRP-96-15, School of Computer Science, The University of Birmingham, September 1996.
Riccardo Poli. Genetic programming for image analysis. In John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 363–368, Stanford University, CA, USA, 28–31 July 1996. MIT Press.
Riccardo Poli. Some steps towards a form of parallel distributed genetic programming. In Proceedings of the First On-line Workshop on Soft Computing, August 1996.
Riccardo Poli. 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, 1997.
Stuart C. Shapiro. Encyclopedia of Artificial Intelligence. Wiley, New York, second edition, 1992.
Eric V. Siegel. Competitively evolving decision trees against fixed training cases for natural language processing. In Kenneth E. Kinnear, Jr., editor, Advances in Genetic Programming, chapter 19, pages 409–423. MIT Press, 1994.
Astro Teller and Manuela Veloso. PADO: Learning tree structured algorithms for orchestration into an object recognition system. Technical Report CMU-CS-95101, Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA, 1995.
Paul Walsh and Conor Ryan. Paragen: A novel technique for the autoparallelisation of sequential programs using genetic programming. In John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 406–409, Stanford University, CA, USA, 28–31 July 1996. MIT Press.
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Poli, R. (1997). Parallel distributed genetic programming applied to the evolution of natural language recognisers. In: Corne, D., Shapiro, J.L. (eds) Evolutionary Computing. AISB EC 1997. Lecture Notes in Computer Science, vol 1305. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0027173
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DOI: https://doi.org/10.1007/BFb0027173
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