PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Powiadomienia systemowe
  • Sesja wygasła!
  • Sesja wygasła!
  • Sesja wygasła!
  • Sesja wygasła!
Tytuł artykułu

Assembler Encoding : a new Artificial Neural Network encoding method

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Kodowanie Asemblerowe : nowa metoda kodowania sieci neuronowych
Języki publikacji
EN
Abstrakty
EN
The main goal of the paper is to outline a new Artificial Neural Network (ANN) encoding method called Assembler Encoding (AE). In AE, ANN is encoded in the form of a program (Assembler Encoding Program - AEP) of linear organization and of a structure similar to the structure of a simple assembler program. The task of AEP is to create the so-called Network Definition Matrix (NDM) including the whole information necessary to produce ANN. To create AEPs, and in consequence ANNs, genetic algorithms are used.
PL
Głównym celem artykułu jest przedstawienie Kodowania Asemblerowego czyli nowej metody kodowania sztucznych sieci neuronowych. W Kodowaniu Asemblerowym sieć neuronowa jest zakodowana w postaci programu (AEP - Assembler Encoding Program) o liniowej organizacji i o strukturze podobnej do struktury prostego programu asemblerowego. Zadaniem AEP jest stworzenie tzw. Macierzy Definicji Sieci (NDM - Network Definition Matrix) zawierającej całą informację potrzebną do stworzenia sieci. Tworzenie AEP i w konsekwencji sieci neuronowych odbywa się z wykorzystaniem technik ewolucyjnych.
Rocznik
Strony
395--429
Opis fizyczny
Bibliogr. 33 poz., tab., wykr.
Twórcy
autor
  • Naval University, 81-103 Gdynia, ul. Śmidowicza 69
Bibliografia
  • [1] K. Balakrishnan, V. Honavar, Properties of Genetic Representations of Neural Architectures, Proc. of the World Congress on Neural Networks (WCNN'95), 1995, 807-813.
  • [2] M. V. Butz, Rule - based Evolutionary Online Learning Systems: Learning Bounds, Classification, and Prediction, IlliGAL Report, no. 2004034, 2004.
  • [3] A. Cangelosi, D. Parisi, S. Nolfi, Cell division and migration in a genotype for neural networks, Network: computation in neural systems, 5 (4), 1994, 497-515.
  • [4] J. L. Elman, Learning and development in neural networks: The importance of starting small, Cognition, 48, 1993, 71-99.
  • [5] D. Floreano, J. Urzelai, Evolutionary robots with online self - organization and behavioral fitness, Neural Networks, 13, 2000, 431-443.
  • [6] D. B. Fogel, Evolving neural networks, Biological Cybernetics, 63, 1990, 487-493.
  • [7] D. E. Goldberg, Genetic algorithms in search, optimization and machine learning, Addison Wesley, Reading, Massachusetts, 1989.
  • [8] F. Gruau, Neural network Synthesis Using Cellular Encoding And The Genetic Algorithm, PhD Thesis, Ecole Normale Superieure de Lyon, 1994.
  • [9] K. A. Gruber, J. Baurick, S. J. Louis, Evolution of Complex Behavior Controllers using Genetic Algorithms, http://citeseer.ist.psu.edu
  • [10] D. O. Hebb, The organization of behavior, Wiley, New York, 1949.
  • [11] M. W. Hwang, J. Y. Choi, J. Park, Evolutionary projection neural networks, In Proceedings of the 1997 IEEE International Conference on Evolutionary Computation, ICEC 97, IEEE Press, 1997, 667-671.
  • [12] H. Kitano, Designing neural networks using genetic algorithms with graph generation system, Complex Systems, 4, 1990, 461-476.
  • [13] K. Krawiec, B. Bhanu, Visual Learning by Coevolutionary Feature Synthesis, IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, 35, 2005, 409-425.
  • [14] R. I. W. Lang, A Future for Dynamic Neural Networks, Technical Report no. CYB/1/PG/RIWL/V1.0, University of Reading, UK, 2000.
  • [15] S. Luke, L. Spector, Evolving Graphs and Networks with Edge Encoding: Preliminary Report, in: John R. Koza, editor, Late Breaking Papers at the Genetic Programming, 1996, Conference Stanford University July 28-31, pages 117-124, Stanford University, CA, USA, Stanford Bookstore, 1996.
  • [16] G. F. Miller, P. M. Todd, S. U. Hegde, Designing Neural Networks Using Genetic Algorithms, Proceedings of the Third International Conference on Genetic Algorithms, 1989, 379-384, of Schaffer J. D.
  • [17] D. E. Moriarty, R. Miikkulainen, Forming Neural Networks Through Efficient and Adaptive Coevolution, Evolutionary Computation, 5(4), 1998, 373-399.
  • [18] D. E. Moriarty, Symbiotic Evolution of Neural Networks in Sequential Decision Tasks, PhD thesis, The University of Texas at Austin, TR UT-AI97-257, 1997.
  • [19] S. Nolfi, D. Parisi, Growing neural networks, in: C. G. Langton (ed.) Artificial Life III, Reading, MA: Addison-Wesley, 1992.
  • [20] P. Nordin, W. Banzhaf, F. Francone, Efficient Evolution of Machine Code for (CISC) Architectures using Blocks and Homologous Crossover, Advances in Genetic Programming III, MIT Press, L. Spector and W. Langdon and U. O'Reilly and P. Angeline, 1999, 275-299.
  • [21] M. Potter, The Design and Analysis of a Computational Model of Cooperative Coevolution, PhD thesis, George Mason University, Fairfax, Virginia, 1997.
  • [22] M. Potter, K. A. De Jong, Evolving neural networks with collaborative species, in: T. I. Oren, L. G. Birta (eds.), Proceedings of the 1995 Summer Computer Simulation Conference, 340-345, The Society of Computer Simulation, 1995.
  • [23] M. A. Potter, K. A. De Jong, A Cooperative Coevolutionary Approach to Function Optimization, The Third Parallel Problem Solving From Nature, Springer Verlag, Jerusalem, Israel, 1994, 249-257.
  • [24] M. A. Potter, K. A. De Jong, Cooperative coevolution: An architecture for evolving coadapted subcomponents, Evolutionary Computation, 8 (1), 2000, 1-29.
  • [25] T. Praczyk, Evolving co - adapted subcomponents in Assembler Encoding, International Journal of Applied Mathematics and Computer Science, 17 (4), 2007.
  • [26] T. Praczyk, Procedure application in Assembler Encoding, Archives of Control Science, vol. 17 (LIII), no. 1, 2007, 71-91.
  • [27] T. Praczyk, Forming dynamical, self - organizing neural networks by means of assembler encoding (in review).
  • [28] T. Praczyk, Using genetic algorithms and assembler encoding to generate neural networks, Computing and Informatics, 2008 (in press).
  • [29] J. W. Prior, Eugenic Evolution for Combinatorial Optimization, Master's thesis, The University of Texas at Austin, TR AI98-268, 1998.
  • [30] J. Urzelai, D. Floreano, Evolution of Adaptive Synapses: Robots with Fast Adaptive Behavior in New Environments, Evolutionary Computation, 9 (4), 2001, 495-524.
  • [31] D. White, P. Ligomenides, GANNet: a genetic algorithm for optimizing topology and weights in neural network design, In Proceedings of International Workshop on Artificial Neural Networks (IWANN 93), Springer Verlag, 1993, 322-327.
  • [32] D. Willshaw, P. Dayan, Optimal plasticity from matrix memories: What goes up must come down, Neural Computation, 2, 1990, 85-93.
  • [33] X. Yao, Evolving Artificial Neural Networks, in: Proceedings of the IEEE, 87 (9), 1999, 1423-1447.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWA9-0022-0032
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.